CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[v12.10.4] - 2024-06-03

Fixed

• Access to the evaluation datasets were shut down by Hugging Face again. It has now been restored.

[v12.10.3] - 2024-06-03

Fixed

• Access to the evaluation datasets were shut down by Hugging Face. It has now been restored.

[v12.10.2] - 2024-05-30

Fixed

• Correctly update logits processors and prefix allowed functions tokens functions for NER datasets when starting generation.
• We now use logprobs for OpenAI models, as this is supported by the chat models now. This is used for all sequence classification based tasks, which currently comprise of sentiment classification, linguistic acceptability, knowledge and common-sense reasoning. This fixes some incorrect evaluations of the newer GPT-4-turbo and GPT-4o models, as they tend to output things like "Sentiment: positive" rather than simply "positive".

[v12.10.1] - 2024-05-28

Fixed

• Now recognises the metadata for the new GPT-4o models correctly. Currently there is a version clash between vllm and tiktoken, meaning that one needs to manually upgrade tiktoken to evaluate GPT-4o - an informative error message notes this to the user now in that case.
• Number of generated tokens for sequence classification tasks has been changed back to 1 (from 3). This makes no difference to open source models, as we only use the logprobs from the first token anyway, but this makes a big difference on multiple choice QA tasks for OpenAI models, as some of them might output things like "a is correct" rather than simply "a". Since we're using word edit distance to the labels, this might accidentally cause the final prediction to be different from "a".
• An error in outlines<=0.0.36 meant that NER evaluations were near-random. Unfortunately, due to a strict outlines requirement in vllm, we cannot enforce outlines>0.0.37 (see this vLLM PR for a future fix). For now, to prevent faulty evaluations, we raise an error, asking the user to manually upgrade outlines if they have an old version.

[v12.10.0] - 2024-05-08

Changed

• Update autoawq to >=0.2.5,<0.3.0, as it now doesn't have a dependency clash with transformers.
• Update vllm to >=0.4.2,<0.5.0, to support new models (such as Phi-3).
• Update torch to >=2.3.0,<3.0.0, as this is required by vllm.

Fixed

• When overriding benchmark configuration parameters in Benchmarker.benchmark then these overridden parameters are now correctly used when building datasets.
• When a generative model was benchmarked on a NER task followed by another task, the structured generation wasn't set up correctly, as we're not re-initialising the model since v12.8.0. We now ensure that the logits processors are re-built for every dataset.

[v12.9.1] - 2024-04-30

Fixed

• Disables the prefix caching of vLLMs, as it has not been implemented with sliding window attention yet, causing re-initialisation errors.
• Updates vllm to >=0.4.1,<0.5.0, as this fixes an issue with benchmarking freezing.

[v12.9.0] - 2024-04-26

Changed

• Update optimum dependency to >=1.19.1,<2.0.0, as it is now compatible with transformers>=4.40.0,<4.41.0.

Fixed

• Pin vllm to v0.4.0, since v0.4.1 has breaking changes and is causing issues with flash attention.
• Catch vLLM error when prefix caching is set for models with sliding window attention, as this is not supported yet in vLLM.

[v12.8.0] - 2024-04-23

Changed

• Updated vllm to >=0.4.0,<0.5.0, which both fixes an issue with multi-gpu benchmarking as well as supporting more models.
• Updated transformers to >=4.40.0,<4.41.0, to support more models.
• Removed the olmo extra, as it is now included in transformers.
• Downgraded outlines to v0.0.34 as any newer version is currently incompatible with vllm. This will be changed back to newer versions when this vLLM PR has been merged and released.

Fixed

• Now does not reload generative models between each evaluation. This both saves some evaluation time, but it also prevents a bug when using multiple GPUs.
• Handle the change from having float logprobs in vLLM to the new Logprob objects.

[v12.7.0] - 2024-04-19

Added

• Added a script to evaluate human performance on datasets. This is a Gradio app which can be run using the command human_evaluate --annotator-id <id>, where annotator-id is the ID of the human annotator (from 0 to 10, inclusive). They will then annotate their answers for validation splits from the iteration corresponding to their annotator ID. All of the annotated results will be stored to scandeval_benchmark_results.jsonl, as usual - note here that this will create a single human entry, where multiple annotators will count as multiple iterations for the same human model.

Fixed

• If a model has a very small maximal context length in its tokeniser configuration then we ignore this value and instead use the default value.
• When a model is generative then we use default context length to be 32,768.
• Now ensures that we use mixed precision when CUDA is available, as this is required by Flash Attention.
• By default we only use flash attention for generative models, as it leads to errors with several encoder models.
• Add missing OpenAI models to the model cache, to checking model existence when no OpenAI key is specified.
• Only imports from the openai package if it has been installed.
• Improved detection of the end-of-chat tokens for instruction tuned models, which previously caused errors when evaluating some instruction tuned models.
• Loading of a pretrained model configuration from the Hugging Face Hub failed when the model is gated and when the cache_dir is specified in AutoConfig.from_pretrained. We now do not set that argument if the model is gated, as a temporary fix.

[v12.6.1] - 2024-04-11

Fixed

• Changed vLLM inference parameters to limit the GPU memory usage during evaluation, which makes it possible to evaluate larger models on the same hardware as previously. Concretely, the gpu_memory_utilization has been raised from 0.9 to 0.95, enforce_eager is set to True, the max_model_len has been reduced from (at most) 10,000 to (at most) 5,000. See this issue for an overview of maximum amount of tokens in each dataset (as of v12.6.0 of ScandEval).
• Removed 1 sample from the Swedish sentiment classification dataset SweReC which was abnormally long, to keep the maximum amount of tokens in the samples below 5,000. Replaced the outlier sample with a new one.
• The number of allowed generated tokens for the Danish summarisation dataset Nordjylland News was mistakenly set to 128, compared to 256 for all other summarisation datasets. This has been fixed now.
• Now correctly detects if autoawq should be installed, when evaluating an AWQ model.
• Reduced transformers dependency to 4.38.x again, as autoawq requires this.
• Do not use BitsAndBytes quantisation if the model is already quantised.

[v12.6.0] - 2024-04-10

Changed

• Updated transformers dependency to >=4.39.3,<4.40.0.

Fixed

• Updated cached OpenAI model metadata.
• When loading local models we now more robustly detect the task of the model (i.e., whether it is a generative model, encoder model or sequence-to-sequence model). This previously prevented evaluation of some local models.
• When detecting whether a local model exists, we now also look for the existence of *.safetensors files.

[v12.5.3] - 2024-04-05

Fixed

• The speed benchmark for OpenAI models was extremely slow, due to an issue with the tokenizer. This has been fixed now.

[v12.5.2] - 2024-04-04

Fixed

• Now using the same label order in the NER task as is in the dataset configuration. From v12.1.0 and onwards these were updated to sorting the labels, but this has resulted in significantly worse performance.
• Added GPT-4-turbo name variations to cached OpenAI model IDs. This means that we'll be able to see if a model ID should be an OpenAI model, without an OpenAI API key.

[v12.5.1] - 2024-04-03

Security

• Now uses an access token to access datasets, allowing the datasets to not be publicly available on the Hugging Face Hub.

[v12.5.0] - 2024-04-02

Added

• We now support evaluation of quantised models, such as GPTQ and AWQ, when the vLLM backend is being used (the default).

Fixed

• Move tensor to the correct device when benchmarking seq-to-seq models (#363). Thanks to @ThomasKluiters for this contribution! 🎉
• Deals with the case where an instruction tuned model does not use any special token at the end of the chat, such as <|im_end|>. This holds for, e.g., Qwen models.
• Better auto-detection of pipeline tag for models on the Hugging Face Hub, in case the tag is not manually set.

[v12.4.0] - 2024-03-27

Added

• Support for Azure OpenAI models! These can now be benchmarked as with any other model, where either the environment variables AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_VERSION need to have been set, or alternatively through the --azure-openai-api-key, --azure-openai-endpoint and --azure-openai-api-version arguments. Thanks to @BramVanroy for all the help regarding the implementation of this 🎉
• We now use the new JSON mode for newer OpenAI models for the NER task, to ensure better JSON generation.
• If an error is thrown during generation with an OpenAI model, which for instance happens when the prompt is caught by the content filter, then we simply return a blank string instead.

Changed

• Updated outlines dependency to v0.0.37, which can now correctly deal with a larger batch size when integrated with vLLM. This results in faster NER evaluation.

Fixed

• Move models to the device before running any inference with it, as this causes issues when flash attention is enabled.
• When benchmarking instruction tuned models, we now ensure that generation stops when the end-of-chat token is reached (such as <|im_end|> and [/INST]). This had a negative performance impact on question answering and summarization, but the remaining tasks were not affected.

[v12.3.2] - 2024-03-19

Fixed

• There is an issue with the underlying outlines package that we use for structured generation, where many of the generations stop prematurely when the batch is too large. We fix this temporarily by lowering the batch size from the entire dataset to the standard 32 when vLLM is used for NER tasks. This will be changed back when the bug is fixed. Follow the progress in this outlines issue.
• Issue when checking if the openai extra needed to be installed, or when the OPENAI_API_KEY needs to be set.
• Setting add_prefix_space=False caused an error during the loading of some tokenizers. To fix this, we only supply the add_prefix_space keyword argument during the loading of the tokenizer if it is True.

[v12.3.1] - 2024-03-13

Fixed

• An issue with Pydantic typing, causing initialisation of Benchmarker to throw an error.

[v12.3.0] - 2024-03-13

Changed

• Updated outlines dependency to >=0.0.36,<0.1. This fixes a race condition caused during evaluation of NER datasets and also includes integration with the transformers library. The existing hardcoded integration has now been removed in favour of the integration in that package.

[v12.2.1] - 2024-03-12

Fixed

• Now includes the transformers integration with outlines directly in the code, which caused issues as they weren't part of the newest outlines release. When it does get included then we will import these as before.
• When evaluating OpenAI models we now do not perform any structured generation, as we do not have access to the logits.

[v12.2.0] - 2024-03-11

Added

• Added the Icelandic common sense reasoning dataset Winogrande-is, being a manually translated version of the English Winogrande dataset. This also means that the HellaSwag-is dataset has been marked as unofficial, and will thus not automatically be included when benchmarking models on the Icelandic common sense reasoning task.

Changed

• Updated vllm dependency to >=0.3.3,<0.4.0, which allows the benchmarking of the new Gemma and OLMO models, without the bug from vLLM v0.3.2.

Fixed

• Do not show message regarding missing flash attention if CUDA is not available.
• Only use bfloat16 as quantisation compute type if it is available and that torch_dtype is set to "bfloat16" in the Hugging Face configuration - otherwise we use float16.
• Since flash attention is now enabled by default, some models couldn't be loaded due to them not supporting it. For these models, flash attention will now be disabled during model loading.
• Now uses a single GPU when finetuning, as previously evaluation would just freeze in this case. In the future we might support multi-GPU finetuning, but since encoder models usually doesn't require multiple GPUs, this is currently not prioritised.

[v12.1.0] - 2024-02-29

Changed

• Flash attention will now default to being used if flash_attn has been installed. If the --use-flash-attention/no-use-flash-attention hasn't been set and the flash_attn package hasn't been installed, then a logging message will be displayed, informing the user.
• Changed backend structured generation framework to outlines from lm-format-enforcer.

Fixed

• Evaluating models on NER tasks used excessive amounts of memory and took very long. This was due to a bug in vLLM v0.3.2, and will be fixed in vLLM v0.3.3. We thus forbid v0.3.2, making it fast again, and we'll remain compatible with the new v0.3.3 when it is released.
• A name clash has been fixed, which caused the MMLU-no dataset to not be run when running all Norwegian datasets.

[v12.0.0] - 2024-02-26

Added

• Now automatically uses multiple GPUs when evaluating generative models with vLLM.
• Now allows "unofficial" datasets, which are datasets which are not included on the official leaderboards and models will only be benchmarked on them if they have been explicitly set using the --dataset argument (or dataset argument if using the Benchmarker API). This allows the inclusion of more datasets, without bloating the evaluation time of "official" evaluations, as well as removing the need to remove old datasets when they are replaced by newer ones.
• The following datasets have been added as unofficial, all datasets that used to be part of ScandEval but has since been replaced:
  1. ARC-da
  2. ARC-no
  3. ARC-sv
  4. ARC-is
  5. ARC-de
  6. ARC-nl
  7. ARC
  8. DaNE
  9. WikiANN-fo
• A more informative error message is now being thrown if additional arguments need to be supplied to evaluate the model, such as --trust-remote-code/trust_remote_code=True.
• When determining a model's maximum sequence length, we now also look at the max_sequence_length attribute of the Hugging Face model configuration.

Changed

• Computation of the BERTScore metric for summarisation tasks are now using the device stated in the benchmark config, making the metric computation significantly faster if a GPU is being used. This defaults to processing 32 samples at a time, which is reduced if OOM errors occur. If OOM errors occur with a batch size of 1 then the scores are computed on CPU, as before.
• Updated transformers dependency to >=4.38.1,<4.39.0, and vllm dependency to >=0.3.2,<0.4.0. This allows the benchmarking of the new Gemma and OLMO models.
• When using the Benchmarker API, the save_results argument now defaults to True.
• The Benchmarker.benchmark method now only returns the list of benchmark results from the given run, rather than all historic benchmark results as well.
• The framework now defaults to using a Hugging Face Hub token when accessing models, if available.

[v11.0.0] - 2024-02-16

Added

• Added arguments to Benchmarker.benchmark (or simply Benchmarker.__call_), corresponding to the same arguments during initialisation. The idea here is that the default parameters are set during initialisation, and then any of these can be changed if needed when performing a concrete evaluation, without having to re-initialise the Benchmarker.
• Added the Danish knowledge datasets danske-talemaader and danish-citizen-tests. Both are multiple choice datasets, where the first one tests knowledge about Danish idioms, and the second one tests knowledge about the Danish society. These replace the machine translated MMLU-da dataset.
• Added a --num-iterations flag (num_iterations in the Python CLI), which controls the number of times each model should be evaluated, defaulting to the usual 10 iterations. This is only meant to be changed for power users, and if it is changed then the resulting scores will not be included in the leaderboards.

Changed

• The default value of the languages are now all languages, rather than only Danish, Swedish and Norwegian.
• Changed all summarisation datasets to use one few-shot example (some were set to 2), and increased the maximum amount of generated tokens to 256 rather than the previous 128, since many of the gold standard summaries are around 200 tokens.

Fixed

• There was an error caused if an old version of the openai package was installed and if the scandeval package was checking if a model exists as an OpenAI model. Now an informative error is thrown if the model is not found on any available platforms, as well as noting the extras that are missing, which prevents the package from checking existence on those platforms.
• Changed the prompt for the English sentiment classification dataset SST5, where it previously stated that the documents were tweets - these have now been renamed to "texts".
• Correctly assess whether the openai extra should be used, which made it impossible to benchmark OpenAI models.
• Disabled lmformatenforcer logging, which happens in the rare case when we're few-shot evaluating a model on NER and there are no JSON-valid tokens to generate.

Removed

• Removed all machine translated ARC datasets, as they had a near 100% correlation with the machine translated version of the MMLU datasets.

[v10.0.1] - 2024-02-12

Fixed

• A prefix space was added to labels in sequence classification tasks that automatically adds a prefix space (such as Mistral). We now check for this and ensure to only manually add prefix space to models that don't automatically do this (such as the Yi models).

[v10.0.0] - 2024-02-12

Added

• Now throws a more informative error when attempting to benchmark a non-generative model on a generative task.

Changed

• Many dependencies are now optional, to make the package less bloated. These extras are jax, for models based on the JAX framework, generative for evaluating generative models, olmo for models based on the OLMO architecture, openai for evaluating OpenAI models, and all to install all of them.
• Updated many dependencies. In particular now uses openai version 1.x.x, which required some changes to the code base as they changed their API.
• Changed the --dataset-task CLI argument (dataset_task in the Python API) to --task (task). This is now the preferred way to choose what to benchmark a model on, rather than remembering all the names of the datasets. E.g., to benchmark a model on all Danish question-answering datasets, we call scandeval -m <model_id> -l da -t question-answering. All the names of the tasks is shown in scandeval --help.
• Renamed the --no-ignore-duplicates to --force (shorthand: -f), which forces the evaluation, meaning that it evaluates the model even if it has previously been evaluated.
• Renamed the --model-id to --model.

Fixed

• Error when encoding a batch of size 1 with OpenAI models.
• Error when benchmarking OpenAI models on MacOS due to the tiktoken.Encoding object not being picklable.
• Fixed an issue with OOM errors when changing from benchmarking one generative model to another.
• Now allows loading tokenisers that require remote code, if --trust-remote-code has been set.
• Fixed an issue where the max_sequence_length parameter in the Hugging Face model configuration wasn't used to determine the max_model_len parameter in the vllm.LLM initialisation, causing some models not being loaded in vLLM.
• An error occured if a tokenizer had no defined BOS token, which happens for some generative models. It is now set to be equal to the EOS token in that case.
• Fixed error related to the extraction of predicted labels in sequence classification tasks for generative models, which unfairly evaluated generative models that require a prefix space on the labels (which are most of them currently).

Removed

• Removed the -d shorthand for --dataset in the CLI, to encourage the use of -t (--task) and -l (--language) instead.

[v9.3.2] - 2024-02-05

Fixed

• Using model revisions did not work with vLLM models - this has now been fixed. These revisions are specified using the '@' operator in the model ID, e.g., scandeval -m gpt2@main.

[v9.3.1] - 2024-01-31

Fixed

• The prompts were not stripped correctly, causing bad evaluations for sequence classification tasks.

[v9.3.0] - 2024-01-29

Changed

• Now requires transformers versions 4.37.x. As they often introduce breaking changes in minor versions, we now only allow a patch version difference and manually update to 4.38.x when it comes out.
• Swapped primary/secondary metrics for the multiple choice tasks, where we now set MCC as the primary metric and accuracy and secondary. This is due to the fact that MCC handles class imbalance better.
• Removed speculative ngram sampling again, as transformers now requires the batch size to be 1, which doesn't make it any faster than normal.
• Swapped primary/secondary metrics for the multiple choice tasks, where we now set MCC as the primary metric and accuracy and secondary. This is due to the fact that MCC handles class imbalance better.
• Number of generated tokens for sequence classification tasks has been changed back to 3 (from 1). This makes no difference to open source models, as we only use the logprobs from the first token anyway, but it does make a difference to closed source models where the logprobs are not available (like OpenAI's chat models), as we're instead calculating word edit distance to the labels.

Fixed

• Prevents FP16 overflow by using -1e3 instead of -1e9 for ~0% probability logprobs during generation with vLLM.
• Avoids excessive disk usage by not caching processed datasets to disk, as we are never using the cached versions anyway.
• We now only strip the prompts if the model's tokenizer includes a prefix space when tokenizing the labels.
• When testing a model's maximum sequence length, we put dummy inputs into them. This causes errors if the dummy inputs are one of the special tokens. Since the special tokens have not always been set up in the tokenizer, we instead rely on a heuristic that the 100th token ID is not a special token.
• An import depended on vllm, which is not installed on non-Linux devices, causing an ImportError. This has now been removed.
• Fixed an issue where structured generation wasn't triggered when vLLM wasn't available.

[v9.2.0] - 2024-01-24

Added

• Added (the English) datasets MMLU, ARC and HellaSwag, as well as Norwegian and Icelandic translations of it. Now the knowledge and common-sense-reasoning tasks are covered in all supported languages except Faroese (i.e., da, sv, no, is, de, nl & en).
• Now uses speculative ngram sampling for text generation when vLLM is not available. This has no effect on performance and increases evaluation speed by 3x on generation heavy tasks like NER and summarization.
• Added structured generation for the NER task, which enables the models to (almost) always output correct JSON, separating the NER capabilities from the JSON capabilities. JSON can be tested separately in a (future) coding benchmark.
• Now adds scandeval_version to the output JSONL results, to make it easier to determine when outdated results need re-benchmarking.

Changed

• Swapped primary/secondary metrics for the NER task, as the MISC tag varies too much from dataset to dataset to be meaningful as a primary metric. Now uses micro-average F1-score across all tags except the MISC tag as a primary metric.

Fixed

• There was a bug where all models were removed from disk prior to benchmarking. This will now only happen if the --clear-model-cache flag is set.
• The vllm package cannot be installed when CUDA is not available - this is now neither installed nor used when this is the case, and generative few-shot evaluation is done using the transformers package rather than vllm.
• Previously temperature was wrongly not set for vLLM and OpenAI models, instead defaulting to their 1.0 values. This was due to the fact that this is set in transformers using the do_sample=False argument, which doesn't transfer to the other libraries. This has now been set to 0.0.
• Now catches OpenAI InvalidRequestErrors.
• Removed overly long or repetitive samples in the multiple choice datasets, which caused errors when evaluating OpenAI models on them.
• Now sets the top_k parameter in the vLLM SamplingParams based on the value it has in the GenerationConfig. This caused a discrepancy, as vLLM defaulted to -1 and transformers to 50.
• When loading a model using transformers then the quantized compute dtype is now correctly set to either bfloat16 or float16, depending on the GPU available, rather than the previous float32. This does not affect generation performance.
• Fixed formatting of summarization metrics.
• Removed print output from bert_score during summarization metric computation.
• Now clears GPU memory properly after finishing the benchmark of a generative model with vLLM.

[v9.1.2] - 2024-01-16

Fixed

• When checking if a model has already been benchmarked, we only care about the few_shot parameter if the model is generative.

[v9.1.1] - 2024-01-15

Fixed

• Now adds a generative key to the logged results, to enable parsing few-shot evaluated models correctly when building leaderboards.

[v9.1.0] - 2024-01-14

Changed

• Now only stores the top-10 log probabilities of generated tokens when the generation length is less than 8 tokens. Also now keeps separate caches for each (model, dataset) combination, where it previously had a single cache for each model. Both of these help reduce the memory usage of the model output cache.
• Optimised cache saving/loading a bit, making the waiting time in between iterations slightly shorter.
• Removes the model output cache for a (model, dataset) combination when the benchmarking of the model on the dataset finishes successfully. Also removed indents in model output cache JSON files. Both of these help reducing the disk space used on caching.

Fixed

• Only require generative models to output logprobs if the dataset is of a task that requires it. This caused the benchmarking to use excessive memory when benchmarking datasets that require long generative outputs, such as NER.

Removed

• Removed some vLLM logging.

[v9.0.0] - 2024-01-12

Added

• Now caches the completions of open source generative models, which effectively makes benchmarking of these ~33% faster. We cannot store all logits for storage reasons (it quickly gets >100GB in that case), so we instead store the top-100 logits for each generated token, but only if the generated sequence is shorter than 50 tokens. We thus assume that (a) these are the only logits needed, and (b) that the generations don't change. We argue that (a) is the case since we only use the logits in classification tasks, in which case we only use the first token anyway. Further, since we're using a temperature of 0 anyway, the generations will be as close to deterministic as possible (up to small rounding fluctuations of logits, which is negligible). This is a breaking change, since it is not compatible with the previous way we cached OpenAI model outputs.
• Added a new --clear-model-cache flag, which removes the cached models after finishing the benchmarking of each model, to save disk space. This doesn't remove the cached model outputs or datasets.
• Added the following new datasets:
  • fone, a Faroese NER dataset, which replaces the previous wikiann-fo dataset.
  • dansk, a Danish NER dataset, which replaces the previous dane dataset.
  • norquad, a Norwegian question answering dataset, which replaces the previous scandiqa-no dataset.
  • Danish, Swedish, German and Dutch versions of the MMLU, ARC and HellaSwag datasets, testing knowledge and common sense reasoning of generative models. These have been machine translated by the University of Oregon using GPT-3.5-turbo. Machine translation is not adequate, of course, so see this as a first version of these kinds of evaluations, to get some benchmarks going asap.
  • squad-nl, a Dutch extract question answering dataset, which is a machine translated version of SQuAD-v2. As with the datasets mentioned above, this is meant as a first version of a Dutch QA dataset, until we have a better one available.
• Added --only-validation-split flag, which only benchmarks the model on the validation split, which is 5-10x smaller than the test split (depending on the dataset). This is especially useful with paid models like OpenAI models. The value of this flag is stored in the benchmark results, so this will be visible on leaderboards.
• Now uses vLLM as the underlying engine for few-shot evaluating generative models, which drastically improves the evaluation speed, as well as requiring less GPU memory.

Changed

• Now compatible withtransformers >= 4.36.2, and this is required now as they have changed their generation API in a breaking manner.
• Now removes all newlines from texts in the summarization task, where previously these were merely "squashed" to single newlines. This makes the separation of few-shot examples for generative models easier.
• Also removes newlines from the NER task, where these were not removed at all previously.
• Now doesn't force ASCII characters in the NER task for generative models, making the target JSON dictionary more consistent with the input text.
• If a model is stored in the Safetensors format on Hugging Face Hub, then we read out the number of parameters directly from those files. This results in more accurate parameter counts as opposed to loading in the model in 4-bit and counting manually.
• Samples with excessively short or long texts have been removed.
• Adjusted number of few-shot examples in datasets to ensure that the resulting prompt is at most ~3000 tokens long.
• When timeout errors occur when loading a model then we will try again at most 5 times now, where previously we would attempt to re-load it indefinitely.

Fixed

• Removed text2text-generation temporarily from the tags defining generative models, since we do not support the benchmarking of these yet. This will be added back in as soon as we support them.
• Now catches OSErrors when loading Hugging Face model configurations, which happen when there is no config.json file in the model repo.
• When sampling few-shot examples for question answering tasks we previously sampled among examples with context length less than 1024 characters, to keep the prompt short. This is too small for some datasets, so now we dynamically set this threshold based on the dataset itself, starting from 512 and doubling until we have at least the number of desired few-shot examples to choose from.
• Now only sets torch_dtype is CUDA is available, as otherwise errors are caused.
• Previously text generation in a batch would be stopped if any of the samples in the batch reached the stopping criteria, causing a lot of incomplete completions. Now the model continues to generate text until the entire batch is complete, and the excess generation is removed afterwards.
• When benchmarking encoder models on QA tasks the contexts are split up if they exceed the model's context length. The stride value used caused errors in rare cases where the model's maximum context length was really small (128). This has been fixed now.
• Now sets ignore_mismatched_sizes when loading models if the model cannot be loaded otherwise. This previously caused some issues when loading certain models.
• Fixed bug where some encoder models did not work properly when loaded in with FP16 mixed precision due to overflow. We now load in models with BF16 as these have a larger range, but fall back to FP16 if BF16 is not available. If both lead to overflow then we attempt again with full FP32, and lastly throw an informative error and block evaluation if the overflow persists.
• When few-shot evaluating models on NER tasks, we are now more lenient towards the generated model output. Instead of taking the output as-is, we are now extracting the first dictionary (enclosed in curly brackets), as well as replacing all single apostrophes (') with double ones (").
• If a model is already pre-quantized then we will not attempt to quantize it as well.

[v8.2.1] - 2023-12-20

Fixed

• Removed the non-existent IsReC, FoReC and FoQA datasets.

[v8.2.0] - 2023-12-20

Added

• Added the following new datasets:
  • sb10k, a German sentiment classification dataset.
  • dutch-social, a Dutch sentiment classification dataset.
  • sst5, an English sentiment classification dataset.
  • germeval, a German NER dataset.
  • conll-nl, a Dutch NER dataset.
  • conll-en, an English NER dataset.
  • scala-de, a German linguistic acceptability dataset.
  • scala-nl, a Dutch linguistic acceptability dataset.
  • scala-en, an English linguistic acceptability dataset.
  • nqii, an Icelandic extractive question answering dataset.
  • germanquad, a German extractive question answering dataset.
  • squad, an English extractive question answering dataset.
  • cnn-dailymail, an English summarization dataset.

Fixed

• Fixed bug with question answering benchmarking when the answer was a proper subset of the first token in the context, causing errors when benchmarking some models.
• Some models have been stored in mixed precision as well as containing an implementation of layer normalisation which is incompatible with such mixed precision. When loading models we now only load in mixed precision if torch_dtype has been specified in the Hugging Face model configuration (as with the Mistral model, for instance).
• When sampling examples to use in few-shot prompts in a sequence classification, we previously required that the samples are stratified with respect to the labels. This caused an issue if the dataset did not contain all labels, so now we only stratify with respect to the labels present in the dataset.
• When few-shot benchmarking on question answering datasets we previously only used the samples whose contexts were at most 512 characters long. This turns out to be too few for germeval, so this has been upped to 1024.

[v8.1.0] - 2023-12-04

Added

• Now added support for text-to-text tasks, which include tasks such as abstractive summarization, abstractive question-answering and translation. These can only be benchmarked with generative models. In this release, this includes the following datasets:
  • nordjylland-news, a Danish summarization dataset based on news articles.
  • swedn, a Swedish summarization dataset based on news articles.
  • no-sammendrag, a Norwegian summarization dataset based on news articles.
  • rrn, an Icelandic summarization dataset based on news articles.
  • mlsum, a German summarization dataset based on news articles.
  • wiki-lingua-nl, a Dutch summarization dataset based on WikiHow articles. These are all of the task summarization, meaning that they can also all be run using scandeval --dataset-task summarization --model-id <model_id>.
• A --use-flash-attention flag has been added, which enables Flash Attention 2.0, which is required by some models, such as Mistral-based ones. If flash-attn has not been installed then an informative error message will be raised. Thanks to @peter-sk for this contribution! 🎉

Changed

• Now uses 8-bit AdamW whenever CUDA is available, as opposed to regular AdamW. Experiments shows that this does not affect benchmarking performance, but reduces memory usage and thus allows benchmarking of larger models

Fixed

• A bug was removed which caused some overlap between the dataset splits of the ScandiQA datasets.
• Now allows loading in models in the data type that they were trained in, which previously caused errors if they weren't trained in float32.

[v8.0.0] - 2023-11-29

Added

• Support for few-shot evaluation of decoder models, both from the Hugging Face Hub and OpenAI models. This currently happens automatically when specifying a generative model from the Hugging Face Hub, and with all OpenAI models.
• Now stores model caches in separate directories, enabling parallel evaluations. Thanks to @KennethEnevoldsen for this contribution! 🎉
• Added --device argument to the CLI, which can be used to overwrite the automatic detection of device (CPU, CUDA GPU, MPS GPU, TPU) to use.
• Added --trust-remote-code/--no-trust-remote-code argument to the CLI, as some models require this flag to be loaded. It defaults to False for security reasons, however.
• Added --load-in-4bit/--no-load-in-4bit argument to the CLI, which can be used to overwrite the automatic 4bit loading of models. By default only generative models will be loaded in 4bit, and only if a CUDA GPU is available, as this is required by the underlying bitsandbytes package.
• Now manually adjusts the maximum sequence length of a model to ensure that the reported maximum length is correct.

Changed

• Now only supports Python 3.10 and above.
• Changed the variation in the speed benchmark. Rather than using a fixed length document and computing iterations per second, it now uses varied length documents and computes tokens per second. This also has the added benefit of being able to better compare models with varying level of maximum sequence lengths. Further, it now uses GPU rather than CPU to accomodate 4-bit models, as these cannot be run on CPU.
• Changed the --model-framework argument to --framework.
• Changed the --use-auth-token and --auth-token arguments to --use-token and --token, reflecting the same change in the transformers package.
• Now reports all model parameters, rather than just the trainable ones.
• Now uses 8-bit AdamW optimizer when CUDA is available rather than the default AdamW, to save memory when working with larger models.

Removed

• Previously generative models had their maximum sequence length altered by subtracting their padding token ID. This is not needed anymore and have been removed.

Fixed

• Handles timeouts better now, when fetching models from the Hugging Face Hub. Instead of simply throwing the error, cancelling the benchmarking process, it simply tries again until the connection is up again.
• Some models output both logits and hidden states, which caused unnecessary out-of-memory issues. This is now handled using the preprocess_logits_for_metrics argument in Trainer.
• Now catches errors while loading model configurations.

[v7.1.1] - 2023-07-01

Fixed

• The feature names of the NER datasets have been changed, so the code have been updated to reflect this.

[v7.1.0] - 2023-05-15

Added

• Added support for the NorBERT3 models.

[v7.0.0] - 2023-05-13

Changed

• Now uses PyTorch 2.0, which (among other things) includes more control over the MPS. This means that MPS out of memory errors will now be caught and dealt with like CUDA out of memory errors, and we clear the MPS cache in between runs.

Fixed

• Ensure that type_vocab_size is not changed if it was previously set to 0. This caused issues for some models when benchmarking question answering tasks.

[v6.3.0] - 2023-04-12

Added

• Now added support for benchmarking local models in the Hugging Face format (i.e., saved with the save_pretrained method). This automatically detects the framework based on the file extension, but can also be set using the new --model-framework argument. Thanks to @peter-sk for implementing this! :tada:

Fixed

• Now handles word-token alignment properly with SentencePiece tokenisers, which caused some models not being able to be benchmarked on token classification tasks.
• Now handles UNK tokens during word-token alignment, where it locates the word that is being tokenised into the UNK token, extracting the original value of the UNK token and replacing the token by that value.

[v6.2.4] - 2023-03-10

Fixed

• If the Hugging Face Hub is down, throwing a HfHubHTTPError, then catch it, wait 30 seconds, and try again.
• Now always fixes the model_max_length attribute of the tokenizer, to prevent index errors during finetuning.

Changed

• Changed raise-error-on-invalid-model to raise-errors. The flag now raises all errors instead of skipping the model evaluations, which can be used for debugging.

[v6.2.3] - 2023-02-27

Fixed

• Ensure that the max_position_embeddings fix from v6.2.2 only occurs if the tokenizer has a padding token, as this is used to set the model_max_length.
• If a model only has a JAX model but also has tags on the Hugging Face Hub from another framework, then re-try the evaluation with from_flax set to True.

[v6.2.2] - 2023-02-25

Fixed

• If max_position_embeddings is smaller than any of the context lengths specified in model_max_length and max_model_input_sizes then we use that as the the tokenization max length. This avoids dimension errors related to truncation.

[v6.2.1] - 2023-02-22

Fixed

• Now does not include models with the word "finetuned" in their name when benchmarking all models. These can still be benchmarked if specified directly.

[v6.2.0] - 2023-01-09

Changed

• Does not include by default models which indicate in their name that they're using more than a billion parameters, such as EleutherAI/gpt-j-6B.

Fixed

• Now sets the default language for the (upcoming) XMOD models.
• If a model's token_type_embeddings layer has size (1, ...) when benchmarking the model for question answering, it is expanded to size (2, ...) with the second row being randomly initialised. This is required as question answering tasks need a least two token type embeddings.
• Now catches OSError when loading tokenizers.

[v6.1.1] - 2023-01-02

Fixed

• Fixed error where some tokenizers did not have special token IDs registered.
• Now catches JSONDecodeError when loading tokenizers.
• Now catches KeyError when loading model configurations.

[v6.1.0] - 2022-12-29

Added

• Added model inference speed estimation benchmark. This can now be run by setting either task or dataset to "speed". E.g., scandeval -m <model_id> -d speed or scandeval -m <model_id> -dt speed. This runs 10 iterations of 100 model inferences on a document of length 2,600 (the document "This is a dummy document. " repeated 100 times). The inference speed includes tokenization, and is powered by the pyinfer package.

[v6.0.1] - 2022-12-28

Fixed

• Added prefix space to DeBERTa models.
• Now automatically changes a model's type_vocab_size to at least 2 when benchmarking the model on question-answering tasks. This previously caused an error when a model config had it set to 1.

[v6.0.0] - 2022-12-24

Added

• Added support for decoder models such as the GPT-series.
• Added new Swedish sentiment classification dataset, SweReC, which is not aspect-based, contrary to the previous ABSAbank-Imm dataset. This dataset is a three-way classification task into the classical positive, neutral and negative classes, thereby establishing uniformity between the sentiment classification datasets in the different languages. The dataset comes from reviews from both se.trustpilot.com and reco.se, and has been created by Kristoffer Svensson as part of his Bachelor thesis "Sentiment Analysis With Convolutional Neural Networks: Classifying sentiment in Swedish reviews".
• Added historic BERT models from dbmdz as part of the default multilingual list.
• Added the --batch-size argument, which can be used to manually select a batch size. Must be among 1, 2, 4, 8, 16 and 32.

Removed

• As SweReC is a drop-in replacement for ABSAbank-Imm, the latter has been removed from the ScandEval benchmark.

Fixed

• Now deals with an issue with DeBERTaV2 models where pooler_hidden_size has been set to a value different to hidden_size in its configuration, which made it impossible to do sequence classification with the model. The former is now forced to be the same as the latter, fixing the issue.
• Now ensures that tokenizers, model configurations and metrics are cached to the ScandEval cache, rather than the default Hugging Face cache.
• Previously, if a model's context length was greater than 1,000 it would be reduced to 512, since an unset context length results in a very large model_max_length value of the tokenizer. This conflicted with longformer-style models whose context length actually was greater than 1,000, so now this upper bound has been increased to 100,000.
• Now includes sacremoses as a dependency, as this is required by some tokenizers.
• Converted the id column in ScandiQA to a string, to avoid integer overflow errors during preprocessing.
• If there is a torch operation which does not have a deterministic component, then a warning will be issued instead of raising an error.

[v5.0.0] - 2022-11-03

Added

• A new argument, ignore_duplicates (or --ignore-duplicates/--no-ignore-duplicates in the CLI) further ignores an evaluation if it has previously been evaluated. This argument defaults to True.
• Now stores the task and the dataset languages to the evaluation file with each evaluation.
• Now stores model metadata to the scandeval_benchmark_results file. Currently, this includes the number of trainable model parameters, the size of the model's vocabulary and the model's maximum sequence length.

Changed

• Evaluation results are now saved in a JSONL file instead of a JSON file, and results are appended onto the file after every evaluation.
• You can now specify your Hugging Face authentication token in the use_auth_token argument of Benchmarker rather than manually logging in with huggingface-cli login. In the CLI an authentication token can also be applied directly using the new --auth-token argument. If an authentication is provided in this way in the CLI, then there is no need to add the --use-auth-token flag.
• The "random" models have now been renamed to "fresh", to emphasise that they are not random, but instead randomly initialized.
• The fresh models are now task independent, meaning that fresh-xlmr-base will now adapt to the task at hand, rather than having to benchmark, e.g., fresh-xlmr-base-sequence-clf and fresh-xlmr-base-token-clf separately.

Fixed

• ScandEval now works on TPUs.
• Removed bf16 precision, as it only works for some GPUs.
• Should output less transformers logging now.
• Models were previously loaded in twice in the beginning of a benchmark. They are now only loaded in once (but re-loaded during each of the 10 iterations to ensure that we are starting from the same point).
• Changed the model architecture of the fresh-xlmr-base from Roberta to XLMRoberta.
• The --dataset-task is now correctly filtering the datasets benchmarked.
• Some tokenizers are not adding special tokens, despite them having registered them. These are now manually added, to ensure a proper evaluation of the models.

Removed

• Removed support for evaluating finetuned models, as the package was primarily used to benchmark pretrained models anyway, and the change in datasets means that many finetuned models would have been trained on (part of) the test sets, resulting in artificially large scores. For evaluation of finetuned models, please check out the aiai_eval Python package instead.

[v4.0.2] - 2022-07-22

Fixed

• Now garbage collects properly, where previously (from v4 onwards) the model and model_dict were not removed from memory after each run, potentially causing a memory leak.

Added

• Added the HuggingFaceHubDown and NoInternetConnection exceptions, to give more information to the user when benchmarking fails.
• Added unit tests.

[v4.0.1] - 2022-07-14

Fixed

• Removed temporary printing of scores for each iteration.

[v4.0.0] - 2022-07-14

Added

• Compatibility with Apple Silicon. If no CUDA GPU is available then MPS GPUs will automatically be used, if available.
• Added the datasets scala-da, scala-sv, scala-nb, scala-nn, scala-is and scala-fo. These are all linguistic acceptability datasets, being a binary text classification where a sentence has to be marked as grammatically correct or not.
• New randomly initialised ELECTRA-small model available for benchmarking, simply set model-id to either 'random-electra-small-sequence-clf or 'random-electra-small-token-clf'. The randomly initialised XLM-RoBERTa-base model is still available by replacing 'electra-small' with 'xlmr-base'.
• Added --raise-error-on-invalid-model (-r) flag which raises an exception if an invalid model is specified. By default this is off, meaning that it simply skips the model if it is invalid.
• Added --model-language (-ml) and --dataset-language (-dl), which can be used to specify the model/dataset languages to benchmark. The --language (-l) argument will now be used for both models and datasets, where the --model-language and --dataset-language will override --language for models/datasets if specified.
• Added --use-auth-token, which is a flag that can be used when evaluating private models on Hugging Face Hub. This requires that the user has logged in via the huggingface-cli login command.
• Added scripts used to create all the datasets used in ScandEval, to ensure full transparency.

Changed

• Models are now evaluated every 30 training steps (corresponding to having processed 960 training samples) rather than every epoch. This decreases benchmarking time significantly, as early stopping kicks in earlier if the model is not learning anything.
• All training splits of datasets have been truncated to 1,024 samples. This has multiple benefits:
  • Faster benchmarking
  • More reliance on pretraining data
  • Enables consistent comparisons between different languages on the same task.
• Now uses warmup_ratio rather than warmup_steps, to ensure that 10% of the dataset is used to warm up the learning rate.
• All CLI arguments now use hyphens (-) rather than underscores (_). For instance, the --model_id argument has now been changed to --model-id.
• Text classification datasets are now using Matthew's correlation coefficient as metric, following the GLUE custom.
• Now requires PyTorch 1.12.0 or newer, to ensure compatibility with Apple Silicon.
• Renamed the Benchmark class to Benchmarker.

Deprecated

• Deprecated support for evaluating finetuned models, as the package was primarily used to benchmark pretrained models anyway, and the change in datasets means that many finetuned models would have been trained on (part of) the test sets, resulting in artificially large scores. For evaluation of finetuned models, please check out the aiai_eval Python package instead (under development).

Removed

• Removed support for Python 3.7, as this was incompatible with support for Apple Silicon.
• Removed the Danish sentiment analysis datasets twitter-sent, europarl and lcc, and instead using only the angry-tweets dataset for this task.
• Removed datasets dkhate, nordial and dalaj, to ensure a larger amount of benchmark uniformity across languages.
• Removed all part-of-speech datasets from the benchmark, as there was too little variance among the scores to differentiate models properly.
• Removed all dependency parsing datasets from the benchmark, both to focus more on the semantic tasks as that's closer to what is being used in practice, as well as to reduce the benchmarking time, as these datasets took way longer to benchmark than the others, due to the high number of labels.
• Removed the load_dataset function, as all datasets can now be found on the Hugging Face Hub and can thus be loaded using the datasets package. All the datasets can be found at https://huggingface.com/ScandEval.

Fixed

• Now disables tokenizer progress bars properly, using the datasets.utils.disable_progress_bar function.
• Many of the datasets contained duplicate entries. These have now all been fixed.
• The --model-id now works as intended, where previously one was forced to use the shortcut -m.
• Now correctly determines whether a NER dataset contains MISC tags. Previously this required that both B-MISC and I-MISC tags were present in the dataset, where it has now been changed to at least one of them.

[v3.0.0] - 2022-04-19

Changed

• During finetuning, the i'th model will only be evaluated on the i'th bootstrapped dataset. This ensures that there will always be 10 scores, no matter if we're finetuning or purely evaluating, which means that the confidence intervals will be more comparable.

Fixed

• Now sets seed in TrainingArguments rather than setting it explicitly in PyTorch. This has the added bonus of ensuring that the DataLoaders used during training also uses this seed, ensuring better reproducibility.
• Initialises model parameters with (fixed) different seeds during every iteration, to ensure variability and reproducibility.
• Explicitly uses the PyTorch implementation of AdamW now, rather than the (deprecated) transformers implementation.
• Fixed an error when a tokenizer has max_model_input_sizes set, but it being empty. In this case, the default truncation length is set to 512.

[v2.3.2] - 2022-02-11

Fixed

• Fixed a bug where a model's framework and pipeline tag were indistinguishable, as they are both using the same tag-white tag now.

[v2.3.1] - 2022-02-11

Fixed

• Changed the tag-red, which referred to the HTML class containing the model framework, to tag-white. This caused models to not be benchmarkable, as their framework could not be determined.

[v2.3.0] - 2022-01-20

Added

• Specific branches/commits/tags can now be benchmarked, using the @ delimiter. For instance, scandeval -m model_id@commit_hash will benchmark the model with model ID model_id, stored at commit with hash commit_hash. Thanks to @versae for contributing! 🎉

[v2.2.0] - 2022-01-18

Added

• Added more label synonyms for the DKHate dataset.

[v2.1.0] - 2022-01-17

Added

• Added support for flax models. Thanks to @versae for contributing! 🎉

[v2.0.0] - 2022-01-07

Fixed

• Changed the anonymisation procedure for the tweet datasets angry-tweets and twitter-sent, now replacing user names by @USER and links by [LINK].

[v1.5.9] - 2021-12-14

Fixed

• Now removing all empty documents from datasets, as well as catching KeyError when trying to remove empty documents from dataset.

[v1.5.8] - 2021-12-13

Fixed

• Now explicitly removing empty tokenisations from the dataset.

[v1.5.7] - 2021-12-10

Fixed

• Now catching all CUDA error exceptions and treating them as running out of memory. No harm done if this is not the case, however, as the script will simply decrease the batch size until it reaches 1, and if CUDA errors persist then it will skip that benchmark.

[v1.5.6] - 2021-12-10

Fixed

• When benchmarking a token classification dataset with a model whose tokenizer does not have a fast variant yet, this raised an error as the word_ids method of BatchEncoding objects only works when the tokenizer is fast. In that case these word IDs are now computed manually. This can currently handle WordPiece and SentencePiece prefixes (i.e., ## and ▁), and will raise an error if the manual alignment of words and tokens fail.
• Catch the CUDA error CUDA error: CUBLAS_STATUS_ALLOC_FAILED, which in this case is due to OOM.

[v1.5.5] - 2021-12-08

Fixed

• Deal with CUDA OOM errors when they occur on a replica, when multiple cores are used.

[v1.5.4] - 2021-12-08

Fixed

• Remove reference to trainer when CUDA OOM error is dealt with.

[v1.5.3] - 2021-12-08

Fixed

• Only try to to merge the id2label and label2id conversions if the model is finetuned. This caused some errors when a model was not finetuned but somehow still had conversion dictionaries.

[v1.5.2] - 2021-12-08

Fixed

• Deal with models with tasks feature-extraction or sentence-similarity as if they were fill-mask, meaning assume that they are merely pretrained models, rather than finetuned.

[v1.5.1] - 2021-11-27

Fixed

• Fixed bug when evaluating a finetuned model.

[v1.5.0] - 2021-11-26

Changed

• Added progress bar description when evaluating models without finetuning them first.
• Lowered the package requirements to the earliest possible versions.

Removed

• Removed support for TensorFlow and Jax models, due to them not working properly anyway. They might be included at a later point, properly.

## [v1.4.0] - 2021-11-25

Changed

• Now also outputting aggregated metrics in the resulting scandeval_benchmark_results.json file. This json file now has keys raw_metrics and total, with raw_metrics containing the previous (raw) scores, and the value of the new total key has aggregated scores (means and standard errors).

[v1.3.8] - 2021-11-25

Changed

• All training/evaluation progress bars are now removed when they are finished, and the training progress bar has no total anymore, as it was misleading.

[v1.3.7] - 2021-11-25

Fixed

• Removed transformers logging during evaluation as well.

[v1.3.6] - 2021-11-25

Changed

• Now only updating the list of benchmarks in the Benchmark during initialisation, and also logs it. This should make subsequent calls to the benchmark method faster.

Fixed

• Removed transformers logging properly.

[v1.3.5] - 2021-11-23

Fixed

• Set the number of warmup steps to be the intended one training set pass, where previously it was effectively 8x that amount, due to gradient accumulation.
• Added the NER label synonyms OBJORG=ORG, LOCPRS=LOC, LOCORG=LOC and ORGPRS=ORG.
• Explicitly added numpy to the install_requires list. This is normally not a problem, as it's a requirement for other required packages, but this depends on the order in which the requirements are installed. This avoids such errors caused by misordering the requirements.

[v1.3.4] - 2021-11-11

Fixed

• Indexing error during synonym setup of finetuned models.

[v1.3.3] - 2021-11-11

Fixed

• When a finetuned model has labels which are synonyms of each other, they are now properly treated as synonyms, where previously this caused the model to have misaligned id2label and label2id conversion dictionaries.

[v1.3.2] - 2021-11-11

Fixed

• Added the NER label synonyms GPE_LOC=LOC, GPE_ORG=ORG, LOC/ORG=LOC, ORG/PRS=ORG, OBJ/ORG=ORG, as Norwegian and Swedish models tend to use these.

[v1.3.1] - 2021-11-11

Fixed

• Fixed a bug in label synonyms when benchmarking a finetuned spaCy for NER.

[v1.3.0] - 2021-11-11

Added

• Added label synonyms for NER benchmarking, which will enforce a more fair comparison of finetuned NER models, if the models have been trained on datasets with different labelling (e.g., Person instead of PER).

[v1.2.1] - 2021-11-11

Removed

• Properly removed the Icelandic WikiANN-IS data files. It was removed from the package, but the underlying files were still lying in the repository.

[v1.2.0] - 2021-10-15

Added

• Added the Icelandic NER dataset MIM-GOLD-NER. This can now be loaded as mim-gold-ner in the Benchmark class and through the CLI.

Removed

• Removed the Icelandic WikiANN-IS dataset, as this has now been replaced by the MIM-GOLD-NER dataset.

[v1.1.3] - 2021-10-04

Fixed

• Added truncation and padding when tokenising token classification datasets.

[v1.1.2] - 2021-09-27

Fixed

• Missing dependency parsing tags.

[v1.1.1] - 2021-09-27

Fixed

• Reduce validation batch size if CUDA runs out of memory, rather than only reducing training batch size.

[v1.1.0] - 2021-09-13

Added

• Added Icelandic and Faroese translations of the Norwegian NoReC sentiment analysis dataset. These can be loaded as norec-is and norec-fo, respectively.

Changed

• When loading datasets with load_dataset, the result is now four dataframes, rather than dictionaries. As the data can be accessed in the same way as with dictionaries, this maintains backwards compatibility.
• If a finetuned NER model has been trained on NER tags not present amongst the ones in the dataset, then these are either converted to MISC tags (if these are present in the dataset) and otherwise O tags. This will make the benchmarking of finetuned diverse NER models more fair.

Fixed

• There was an error when a SpaCy model was benchmarked on a dataset that it was not trained on. It now raises an appropriate InvalidBenchmark exception, and will be skipped in the CLI and with the Benchmark class.

[v1.0.2] - 2021-09-09

Fixed

• Replaced abbreviations with spaces, such as "o s v" in the SDT corpus, with their proper version "o.s.v.".

[v1.0.1] - 2021-09-09

Fixed

• The URLs for the wikiann-is and wikiann-fo were wrong and have been corrected.

[v1.0.0] - 2021-09-09

Added

• Added the Icelandic and Faroese WikiANN datasets, for NER evaluation. They can be loaded as wikiann-is and wikiann-fo in the CLI and via the Benchmark class.
• Added the Icelandic and Faroese parts of the Universal Dependencies datasets, containing POS and dependency parsing tags. They can be loaded as idt-pos, idt-dep, fdt-pos and fdt-dep, respectively.

[v0.17.0] - 2021-09-09

Added

• Added the Dataset for Linguistic Acceptability Judgments (DaLaJ) dataset, which is here used as a binary classification dataset, in which sentences have to be classified as correct Swedish or not. It can be loaded as dalaj in the CLI and via the Benchmark class.
• Added the ABSAbank-Imm dataset, which is an aspect-based sentiment analysis dataset in Swedish, namely, the sentiment towards immigration. The original dataset featured a floating point score between 0 and 5, which has been reduced to a classifical three-way classification (negative, neutral and positive). It can be loaded as absabank-imm in the CLI and via the Benchmark class.
• Added the POS and dependency parsing parts of the Swedish Dependency Treebank (SDT). They can be loaded as sdt-pos and sdt-dep in the CLI and via the Benchmark class.
• Added the Stockholm-Umeå corpus 3.0 (SUC 3.0), a Swedish NER dataset. It can be loaded as suc3 in the CLI and via the Benchmark class.
• Added abstract NerBenchmark, PosBenchmark and DepBenchmark classes, to ensure uniformity.

Changed

• Uniformised all the NER datasets. They now all only have the NER tags PER, LOC, ORG and MISC.
• Uniformised all the dependency parsing datasets. They now all only have the main dependency parsing tags, without the subtags (so acl:cleft has been changed to acl, for instance).
• Changed the columns in all text classification datasets to text and label, to make it more uniform.

[v0.16.0] - 2021-09-07

Fixed

• Upped the number index tokens for dependency parsing from 100 to 512. This will need to be done better in the future, but is a fix for now.

Added

• Added the random models random-roberta-sequence-clf and random-roberta-token-clf to the default list of model IDs when benchmarking all models.

[v0.15.1] - 2021-09-03

Fixed

• The list of dependency tags in the ndt-nb-dep and ndt-nn-dep were wrong. They have now been changed to all the tags occurring in the training sets.
• The europarl_sent data folder has now been renamed to europarl, so that it can be loaded correctly with load_dataset.

[v0.15.0] - 2021-09-02

Added

• Added the Bokmål and Nynorsk POS and DEP parts of the Norwegian Dependency Treebank dataset (NDT). They can be loaded as ndt-nb-pos, ndt-nn-pos, ndt-nb-dep and ndt-nn-dep, respectively, from the CLI and the Benchmark class.

Removed

• Removed the EuroparlSubj and TwitterSubj datasets, as they were too easy and did not really differentiate models.
• Removed the abstract SentimentClassificationBenchmark and BinaryClassificationBenchmark, to simplify the classes. There is now only one TextClassificationBenchmark, which always evaluates with macro-F1.

Changed

• Changed the name of europarl-sent to europarl, as europarl-subj now does not exist anymore.
• Changed the nordial dataset to the original 4-way classification dataset.

[v0.14.1] - 2021-09-02

Fixed

• Remove duplicate model IDs when calling the CLI or Benchmark class without any specified model IDs.

[v0.14.0] - 2021-08-31

Added

• Added the Bokmål and Nynorsk parts of the NorNE dataset, for named entity recognition. They can be loaded with the norne-nb and norne-nn names.
• There is now a load_dataset function, which can load any dataset, using the dataset's name (same name as in the CLI). For instance, load_dataset('angry-tweets') loads the AngryTweets dataset. This can be imported directly from the package: from scandeval import load_dataset. The individual dataset loading functions can still be imported as before; e.g., from scandeval.datasets import load_angry_tweets.

Changed

• Refactored folder structure with benchmarks and datasets.
• Separated dane and dane-no-misc into two distinct benchmark classes. The dane-no-misc can now also be loaded with the load_dataset function.

[v0.13.0] - 2021-08-30

Added

• Added the Norwegian Review Corpus (NoReC), a sentiment classification dataset in Norwegian.
• Added the Bokmål/Nynorsk part of the Norwegian Dialect dataset (NorDial), a binary classification dataset in Norwegian.

Changed

• Changed the early stopping patience to 2 + 1000 // len(train) from 2 + 250 // len(train), to allow more patience (and thus, more stability), for smaller datasets.

[v0.12.0] - 2021-08-26

Changed

• Merged the lcc1 and lcc2 datasets into one lcc dataset, which is reasonable as they have been annotated by the same person. The lcc2 dataset was too small to give reasonable benchmarking results.
• Renamed the europarl2 dataset to europarl_sent

Removed

• Removed the europarl1 dataset, as it was too small to give reliable benchmarking results. This dataset could not simply be added to the europarl2 dataset, as with the new lcc dataset, as the annotaters are not the same.

Fixed

• If errors occur during benchmarking, then garbage collect before skipping to the next benchmark, to avoid memory issues.

[v0.11.2] - 2021-08-25

Fixed

• Issue with model_max_length in tokenizer meant that models with an ill-set value of max_position_embeddings could not be benchmarked. Now, if model_max_length is not set then the minimal value of the sizes in max_model_input_sizes will be used (which is usually 512).

Changed

• Disabling CUDNN benchmark when using the pytorch framework, to enforce better reproducibility.

[v0.11.1] - 2021-08-24

Changed

• Rather than bootstrapping the training dataset and using the results to compute an estimator of the standard deviation, the same training dataset is trained on all ten times, and the mean of these along with a confidence interval is outputted.

Fixed

• Updated the model metadata fetching to the new HTML structure of the HuggingFace Hub.
• A random seed is now set for all libraries, via the transformers.set_seed function.
• Always update the list of all the benchmarks when calling the Benchmark.benchmark method, to allow for possibility of setting new benchmark parameters after initialisation.

[v0.11.0] - 2021-08-23

Added

• The subjective/objective part of the TwitterSent and Europarl2 datasets have now been added as binary classification tasks, called TwitterSubj and EuroparlSubj, respectively. These can now be benchmarked with the Benchmark class and the CLI using the twitter-subj and europarl-subj names, respectively.
• Added an abstract BinaryClassificationBenchmark, to streamline the binary classification benchmark datasets, which now includes the DKHate, TwitterSubj and EuroparlSubj datasets.

[v0.10.1] - 2021-08-20

Fixed

• Now catches IndexError during training.

[v0.10.0] - 2021-08-20

Fixed

• Properly filters by languages now via the language argument in the CLI and the Benchmark class. As HuggingFace Hub does not have a keyword for language, a search for language also means that any other non-language tag with that name also shows up in the results. These are now manually removed. This means it takes a few more seconds to compile the model list, but it will at least be accurate.
• In case model_max_length has not been set in a model configuration, it defaults to the value of max_position_embeddings. This fixes a problem with some models not being able to be trained on datasets whose texts were too long.
• Now handles the case where a non-classification model, such as a seq-to-seq model, are being benchmarked on a classification dataset.

Added

• All the benchmark classes and Benchmark now has a benchmark method, which does the same as the __call__ method. This is primarily so that it shows up in the Sphinx documentation.
• Added the default LABEL_0 and LABEL_1 label synonyms for NOT and OFF in the DKHate benchmark.
• Added the possibility of benchmarking randomly initialised RoBERTa models, using the model IDs random-roberta-sequence-clf and random-roberta-token-clf.

[v0.9.0] - 2021-08-19

Added

• Added the separate nb (Norwegian Bokmål) and nn (Norwegian Nynorsk) language tags, on top of the general no (Norwegian).
• Added more multilingual models.

Fixed

• SpaCy models was evaluated wrongly on the dane-no-misc dataset, as their MISC predictions was not replaced with O tags.
• When evaluating models finetuned for token classification on a text classification task, a ValueError was raised, rather than an InvalidBenchmark exception.
• If none of the model's labels are among the dataset's labels, and are not even synonyms of them, then raise an InvalidBenchmark. This prevents things like evaluating a finetuned sentiment model on a NER task.
• When evaluate_train was True, this previously evaluated the test set instead.

Changed

• Changed Benchmark API. Now the constructor and the __call__ method have the same arguments, except the model_id and dataset in __call__, where the constructor sets the default values and the __call__ method can change these to specific cases.
• Changed the benchmarking order. Now benchmarks all datasets for a model, before moving on to the next model
• Renamed the multilabel argument to the more descriptive two_labels.
• Updated docstrings to be more accurate.
• Early stopping patience is now set to 2 + 250 // len(train), so that smaller datasets can enjoy a bit more patience, but if the dataset contains at least 250 samples then it will remain at the current 2 patience.

Removed

• Removed learning_rate, batch_size, warmup_steps and num_finetunings arguments from the benchmarks. These are now fixed to 2e-5, 32, 25% of the training dataset and 10, respectively. Note that the batch size will still automatically decrease if the GPU runs out of memory.

[v0.8.0] - 2021-08-18

Changed

• Models are now being trained for much longer, but with an early stopping callback with patience 2. This will enable a more uniform comparison between models that require a different number of finetuning epochs.

Fixed

• There was a bug when evaluating a finetuned PyTorch model on a sequence classification task, if the model had only been trained on a proper subset of the labels present in the dataset.

Removed

• All individual benchmarks have been removed from __init__.py. They can still be imported using their individual modules, for instance from scandeval.dane import DaneBenchmark, but the idea is to use the general Benchmark class instead.

[v0.7.0] - 2021-08-17

Changed

• Always ensure that a model can deal with the labels in the dataset when finetuning. If the model has not been trained on the label, then this will result in the model always getting that label wrong. For instance, this is the case for finetuned NER models not having been trained on MISC tags, if they are being evaluated on the DaNE dataset.

Fixed

• Fixed bug when evaluating SpaCy models.
• Only removing objects at memory cleanup if they exist at all.

[v0.6.0] - 2021-08-15

Added

• When finetuning models, 10% of the training data is used to evaluate the models, which is used to choose the best performing model across all the epochs trained. This will allow for a more fair comparison, as some models degrade over time, while other models need a longer time to train.

Changed

• Uniformised the _log_metrics method for all benchmarks, now only defined in BaseBenchmark.

Fixed

• Garbage collects when downsizing batch size, to not keep all the previous models in memory.
• Typos in logging.

[v0.5.2] - 2021-08-13

Fixed

• Fixed bug when evaluate_train was set to False.

[v0.5.1] - 2021-08-13

Fixed

• The bootstrapping of the datasets is now done properly. Previously the bootstrapped datasets were not converted to HuggingFace Dataset objects.

[v0.5.0] - 2021-08-12

Added

• It is possible to only evaluate on the test sets, to save some time. This can be done in the Benchmark class using the evaluate_train argument, and in the CLI with the --evaluate_train flag.
• Added progress_bar argument to Benchmark to control whether progress bars should be shown, and added the no_progress_bar flag to the CLI for the same reason.

Changed

• Updated epochs and warmup_steps of all the datasets to something more reasonable, enabling better comparisons of the finetuned models.
• Changed calculation of confidence intervals, which is now based on bootstrapping rather than the analytic approach. It will now evaluate ten times on the test set and compute a bootstrap estimate of the standard error, which is uses to compute an interval around the score on the entire test set.

[v0.4.3] - 2021-08-12

Fixed

• RuntimeErrors occuring during training will now raise an InvalidBenchmark exception, which means that the CLI and the Benchmark class will skip it. This is for instance caused when max_length has not been specified in the model config, meaning that the tokeniser does not know how much to truncate.

[v0.4.2] - 2021-08-12

Fixed

• Now catching the error where tokenisation is not possible, due to the model having been trained on a different task than what is present in the dataset. E.g., if a generator model is trained on a classification task.

[v0.4.1] - 2021-08-12

Fixed

• Now catching the error when the model's config does not align with the model class. When using the CLI or Benchmark, these will be skipped.

[v0.4.0] - 2021-08-11

Added

• Added confidence intervals for finetuned models, where there is a 95% likelihood that the true score would belong to the interval, given infinite data from the same distribution. In the case of "raw" pretrained models, this radius is added onto the existing interval, so that both the uncertainty in model initialisation as well as sample size of the validation dataset affects the size of the interval.
• Added garbage collection after each benchmark, which will (hopefully) prevent memory leaking when benchmarking several models.

Changed

• New logo, including the Faroe Islands!
• Allow the possibility to include all languages and/or tasks in the CLI and the Benchmark class.
• Added Icelandic and Faroese to default list of languages in CLI and the Benchmark class.
• The default value for task is now all tasks, which also includes models that haven't been assigned any task on the HuggingFace Hub;
• If a model cannot be trained without running out of CUDA memory, even with a batch size of 1, then the model will be skipped in Benchmark and the CLI.

Fixed

• New model is initialised if CUDA runs out of memory, to ensure that we are now continuing to train the previous model.
• Dependency parsing now implemented properly as two-label classification, with associated UAS and LAS metric computations. Works for pretrained SpaCy models as well as finetuning general language models.

[v0.3.1] - 2021-08-10

Fixed

• Reduces batch size if CUDA runs out of memory during evaluation.
• Loading of text classification datasets now working properly.

[v0.3.0] - 2021-08-10

Changed

• The W036 warning message from SpaCy is no longer shown.

Fixed

• Raise InvalidBenchmark if model cannot be loaded from the HuggingFace Hub.

[v0.2.0] - 2021-08-09

Added

• Added the part-of-speech tagging task from the Danish Dependency Treebank. Can be loaded with load_ddt_pos and used in Benchmark as ddt-pos.
• Added the dependency parsing task from the Danish Dependency Treebank. Can be loaded with load_ddt_ddt and used in Benchmark as ddt-dep.
• Documentation section and link to README
• The Benchmark class and the CLI now accepts a batch_size argument

Changed

• Benchmark arguments languages, tasks, model_ids and datasets have been renamed to language, task, model_id and dataset, to keep it consistent with the CLI.
• When loading datasets, these will now be four dictionaries instead of lists, to allow for distinguishing features and labels.
• batch_size arguments can now only be among 1, 2, 4, 8, 16 and 32, and the corresponding gradient accumulation will be set to 32, 16, 8, 4, 2 and 1, respectively. This is to ensure that all finetuning is done using the same effective batch size, to ensure fair comparisons.
• Batch sizes are automatically halved if the GPU runs out of memory, with gradient accumulation correspondingly doubles.
• Evaluation of SpaCy models on token classification tasks are more accurate.

Fixed

• README typos fixed, and image renders correctly

[v0.1.0] - 2021-08-05

Added

• First beta release
• Features Danish sentiment, hate speech detection and named entity recognition datasets for benchmarking