The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'NER' of the dataset.
Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 23 fields in line 149, saw 25

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/", line 323, in compute
                File "/src/services/worker/src/worker/job_runners/split/", line 88, in compute_first_rows_from_parquet_response
                  rows_index = indexer.get_rows_index(
                File "/src/libs/libcommon/src/libcommon/", line 631, in get_rows_index
                  return RowsIndex(
                File "/src/libs/libcommon/src/libcommon/", line 512, in __init__
                  self.parquet_index = self._init_parquet_index(
                File "/src/libs/libcommon/src/libcommon/", line 529, in _init_parquet_index
                  response = get_previous_step_or_raise(
                File "/src/libs/libcommon/src/libcommon/", line 566, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              During handling of the above exception, another exception occurred:
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/", line 241, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 2216, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 1239, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 1389, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 1044, in __iter__
                  yield from islice(self.ex_iterable, self.n)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/", line 282, in __iter__
                  for key, pa_table in self.generate_tables_fn(**self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/", line 195, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/", line 1843, in __next__
                  return self.get_chunk()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/", line 1985, in get_chunk
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/", line 1923, in read
                  ) =  # type: ignore[attr-defined]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 23 fields in line 149, saw 25

Need help to make the dataset viewer work? Open a discussion for direct support.

Domain Adaptation of Large Language Models

This repo contains the NER dataset used in our ICLR 2024 paper Adapting Large Language Models via Reading Comprehension.

We explore continued pre-training on domain-specific corpora for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to transform large-scale pre-training corpora into reading comprehension texts, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B.

🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗

**************************** Updates ****************************

  • 2024/4/2: Released the raw data splits (train and test) of all the evaluation datasets
  • 2024/1/16: 🎉 Our research paper has been accepted by ICLR 2024!!!🎉
  • 2023/12/19: Released our 13B base models developed from LLaMA-1-13B.
  • 2023/12/8: Released our chat models developed from LLaMA-2-Chat-7B.
  • 2023/9/18: Released our paper, code, data, and base models developed from LLaMA-1-7B.

Domain-Specific LLaMA-1


In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: Biomedicine-LLM, Finance-LLM and Law-LLM, the performances of our AdaptLLM compared to other domain-specific LLMs are:


Moreover, we scale up our base model to LLaMA-1-13B to see if our method is similarly effective for larger-scale models, and the results are consistently positive too: Biomedicine-LLM-13B, Finance-LLM-13B and Law-LLM-13B.

Domain-Specific LLaMA-2-Chat

Our method is also effective for aligned models! LLaMA-2-Chat requires a specific data format, and our reading comprehension can perfectly fit the data format by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: Biomedicine-Chat, Finance-Chat and Law-Chat

Domain-Specific Tasks

Pre-templatized/Formatted Testing Splits

To easily reproduce our prompting results, we have uploaded the filled-in zero/few-shot input instructions and output completions of the test each domain-specific task: biomedicine-tasks, finance-tasks, and law-tasks.

Note: those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.

Raw Datasets

We have also uploaded the raw training and testing splits, for facilitating fine-tuning or other usages:

The other datasets used in our paper have already been available in huggingface, and you can directly load them with the following code:

from datasets import load_dataset

# MQP:
dataset = load_dataset('medical_questions_pairs')
# PubmedQA:
dataset = load_dataset('bigbio/pubmed_qa')
dataset = load_dataset("lex_glue", 'scotus')
# CaseHOLD
dataset = load_dataset("lex_glue", 'case_hold')
dataset = load_dataset("lex_glue", 'unfair_tos')


If you find our work helpful, please cite us:

title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},

and the original dataset:

  author       = {Julio Cesar Salinas Alvarado and
                  Karin Verspoor and
                  Timothy Baldwin},
  title        = {Domain Adaption of Named Entity Recognition to Support Credit Risk
  booktitle    = {{ALTA}},
  pages        = {84--90},
  publisher    = {{ACL}},
  year         = {2015}
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