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metadata
base_model: google/flan-t5-xl
datasets:
  - 2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark-v2
language: en
license: apache-2.0
model_id: flan-t5-xl-job-bias-qlora-seq2seq-cls
model_description: >-
  The model is a multi-label classifier designed to detect various types of bias
  within job descriptions.
developers: Tristan Everitt and Paul Ryan
model_card_authors: See developers
model_card_contact: See developers
repo: https://gitlab.computing.dcu.ie/everitt2/2024-mcm-everitt-ryan
training_regime: >-
  accelerator_config="{'split_batches': False, 'dispatch_batches': None,
  'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False,
  'gradient_accumulation_kwargs': None}", adafactor=false, adam_beta1=0.9,
  adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=false,
  batch_eval_metrics=false, bf16=false, bf16_full_eval=false, data_seed="None",
  dataloader_drop_last=false, dataloader_num_workers=0,
  dataloader_persistent_workers=false, dataloader_pin_memory=true,
  dataloader_prefetch_factor="None", ddp_backend="None",
  ddp_broadcast_buffers="None", ddp_bucket_cap_mb="None",
  ddp_find_unused_parameters="None", ddp_timeout=1800, deepspeed="None",
  disable_tqdm=false, dispatch_batches="None", do_eval=true, do_predict=false,
  do_train=false, eval_accumulation_steps="None", eval_batch_size=8,
  eval_delay=0, eval_do_concat_batches=true, eval_on_start=false,
  eval_steps="None", eval_strategy="epoch", evaluation_strategy="None",
  fp16=false, fp16_backend="auto", fp16_full_eval=false, fp16_opt_level="O1",
  fsdp="[]", fsdp_config="{'min_num_params': 0, 'xla': False, 'xla_fsdp_v2':
  False, 'xla_fsdp_grad_ckpt': False}", fsdp_min_num_params=0,
  fsdp_transformer_layer_cls_to_wrap="None", full_determinism=false,
  generation_config="None", generation_max_length="None",
  generation_num_beams="None", gradient_accumulation_steps=1,
  gradient_checkpointing=false, gradient_checkpointing_kwargs="None",
  greater_is_better=false, group_by_length=false, half_precision_backend="auto",
  ignore_data_skip=false, include_inputs_for_metrics=false, jit_mode_eval=false,
  label_names="None", label_smoothing_factor=0.0, learning_rate=0.001,
  length_column_name="length", load_best_model_at_end=true, local_rank=0,
  lr_scheduler_kwargs="{}", lr_scheduler_type="linear", max_grad_norm=1.0,
  max_steps=-1, metric_for_best_model="loss", mp_parameters="",
  neftune_noise_alpha="None", no_cuda=false, num_train_epochs=3,
  optim="adamw_torch", optim_args="None", optim_target_modules="None",
  past_index=-1, per_device_eval_batch_size=8, per_device_train_batch_size=8,
  per_gpu_eval_batch_size="None", per_gpu_train_batch_size="None",
  predict_with_generate=true, prediction_loss_only=false, ray_scope="last",
  remove_unused_columns=true, report_to="[]",
  restore_callback_states_from_checkpoint=false, resume_from_checkpoint="None",
  seed=42, skip_memory_metrics=true, sortish_sampler=false,
  split_batches="None", tf32="None", torch_compile=false,
  torch_compile_backend="None", torch_compile_mode="None", torchdynamo="None",
  tpu_num_cores="None", train_batch_size=8, use_cpu=false, use_ipex=false,
  use_legacy_prediction_loop=false, use_mps_device=false, warmup_ratio=0.0,
  warmup_steps=0, weight_decay=0.001
results: |2-
                    precision    recall  f1-score   support
      
               age       0.89      0.59      0.71        80
        disability       0.89      0.40      0.55        80
          feminine       0.92      0.90      0.91        80
           general       0.79      0.60      0.68        80
         masculine       0.83      0.68      0.74        80
           neutral       0.37      0.93      0.53        80
            racial       0.89      0.79      0.83        80
         sexuality       0.96      0.81      0.88        80
      
         micro avg       0.72      0.71      0.72       640
         macro avg       0.82      0.71      0.73       640
      weighted avg       0.82      0.71      0.73       640
       samples avg       0.74      0.75      0.74       640
      
compute_infrastructure: |-
  - Linux 5.15.0-78-generic x86_64
  - MemTotal:       1056619068 kB
  - 256 X AMD EPYC 7702 64-Core Processor
  - GPU_0: NVIDIA L40S
software: >-
  python 3.10.12, accelerate 0.32.1, aiohttp 3.9.5, aiosignal 1.3.1, anyio
  4.2.0, argon2-cffi 23.1.0, argon2-cffi-bindings 21.2.0, arrow 1.3.0, asttokens
  2.4.1, async-lru 2.0.4, async-timeout 4.0.3, attrs 23.2.0, awscli 1.33.26,
  Babel 2.14.0, beautifulsoup4 4.12.3, bitsandbytes 0.43.1, bleach 6.1.0,
  blinker 1.4, botocore 1.34.144, certifi 2024.2.2, cffi 1.16.0,
  charset-normalizer 3.3.2, click 8.1.7, cloudpickle 3.0.0, colorama 0.4.6, comm
  0.2.1, cryptography 3.4.8, dask 2024.7.0, datasets 2.20.0, dbus-python 1.2.18,
  debugpy 1.8.0, decorator 5.1.1, defusedxml 0.7.1, dill 0.3.8, distro 1.7.0,
  docutils 0.16, einops 0.8.0, entrypoints 0.4, evaluate 0.4.2, exceptiongroup
  1.2.0, executing 2.0.1, fastjsonschema 2.19.1, filelock 3.13.1, flash-attn
  2.6.1, fqdn 1.5.1, frozenlist 1.4.1, fsspec 2024.2.0, h11 0.14.0, hf_transfer
  0.1.6, httpcore 1.0.2, httplib2 0.20.2, httpx 0.26.0, huggingface-hub 0.23.4,
  idna 3.6, importlib_metadata 8.0.0, iniconfig 2.0.0, ipykernel 6.29.0, ipython
  8.21.0, ipython-genutils 0.2.0, ipywidgets 8.1.1, isoduration 20.11.0, jedi
  0.19.1, jeepney 0.7.1, Jinja2 3.1.3, jmespath 1.0.1, joblib 1.4.2, json5
  0.9.14, jsonpointer 2.4, jsonschema 4.21.1, jsonschema-specifications
  2023.12.1, jupyter-archive 3.4.0, jupyter_client 7.4.9, jupyter_contrib_core
  0.4.2, jupyter_contrib_nbextensions 0.7.0, jupyter_core 5.7.1, jupyter-events
  0.9.0, jupyter-highlight-selected-word 0.2.0, jupyter-lsp 2.2.2,
  jupyter-nbextensions-configurator 0.6.3, jupyter_server 2.12.5,
  jupyter_server_terminals 0.5.2, jupyterlab 4.1.0, jupyterlab_pygments 0.3.0,
  jupyterlab_server 2.25.2, jupyterlab-widgets 3.0.9, keyring 23.5.0,
  launchpadlib 1.10.16, lazr.restfulclient 0.14.4, lazr.uri 1.0.6, locket 1.0.0,
  lxml 5.1.0, MarkupSafe 2.1.5, matplotlib-inline 0.1.6, mistune 3.0.2,
  more-itertools 8.10.0, mpmath 1.3.0, multidict 6.0.5, multiprocess 0.70.16,
  nbclassic 1.0.0, nbclient 0.9.0, nbconvert 7.14.2, nbformat 5.9.2,
  nest-asyncio 1.6.0, networkx 3.2.1, nltk 3.8.1, notebook 6.5.5, notebook_shim
  0.2.3, numpy 1.26.3, nvidia-cublas-cu12 12.1.3.1, nvidia-cuda-cupti-cu12
  12.1.105, nvidia-cuda-nvrtc-cu12 12.1.105, nvidia-cuda-runtime-cu12 12.1.105,
  nvidia-cudnn-cu12 8.9.2.26, nvidia-cufft-cu12 11.0.2.54, nvidia-curand-cu12
  10.3.2.106, nvidia-cusolver-cu12 11.4.5.107, nvidia-cusparse-cu12 12.1.0.106,
  nvidia-nccl-cu12 2.19.3, nvidia-nvjitlink-cu12 12.3.101, nvidia-nvtx-cu12
  12.1.105, oauthlib 3.2.0, overrides 7.7.0, packaging 23.2, pandas 2.2.2,
  pandocfilters 1.5.1, parso 0.8.3, partd 1.4.2, peft 0.11.1, pexpect 4.9.0,
  pillow 10.2.0, pip 24.1.2, platformdirs 4.2.0, pluggy 1.5.0, polars 1.1.0,
  prometheus-client 0.19.0, prompt-toolkit 3.0.43, protobuf 5.27.2, psutil
  5.9.8, ptyprocess 0.7.0, pure-eval 0.2.2, pyarrow 16.1.0, pyarrow-hotfix 0.6,
  pyasn1 0.6.0, pycparser 2.21, Pygments 2.17.2, PyGObject 3.42.1, PyJWT 2.3.0,
  pyparsing 2.4.7, pytest 8.2.2, python-apt 2.4.0+ubuntu3, python-dateutil
  2.8.2, python-json-logger 2.0.7, pytz 2024.1, PyYAML 6.0.1, pyzmq 24.0.1,
  referencing 0.33.0, regex 2024.5.15, requests 2.32.3, rfc3339-validator 0.1.4,
  rfc3986-validator 0.1.1, rpds-py 0.17.1, rsa 4.7.2, s3transfer 0.10.2,
  safetensors 0.4.3, scikit-learn 1.5.1, scipy 1.14.0, SecretStorage 3.3.1,
  Send2Trash 1.8.2, sentence-transformers 3.0.1, sentencepiece 0.2.0, setuptools
  69.0.3, six 1.16.0, sniffio 1.3.0, soupsieve 2.5, stack-data 0.6.3, sympy
  1.12, tabulate 0.9.0, terminado 0.18.0, threadpoolctl 3.5.0, tiktoken 0.7.0,
  tinycss2 1.2.1, tokenizers 0.19.1, tomli 2.0.1, toolz 0.12.1, torch 2.2.0,
  torchaudio 2.2.0, torchdata 0.7.1, torchtext 0.17.0, torchvision 0.17.0,
  tornado 6.4, tqdm 4.66.4, traitlets 5.14.1, transformers 4.42.4, triton 2.2.0,
  types-python-dateutil 2.8.19.20240106, typing_extensions 4.9.0, tzdata 2024.1,
  uri-template 1.3.0, urllib3 2.2.2, wadllib 1.3.6, wcwidth 0.2.13, webcolors
  1.13, webencodings 0.5.1, websocket-client 1.7.0, wheel 0.42.0,
  widgetsnbextension 4.0.9, xxhash 3.4.1, yarl 1.9.4, zipp 1.0.0
hardware_type: 1 X NVIDIA L40S
hours_used: '1.47'
cloud_provider: N/A
cloud_region: N/A
co2_emitted: N/A
direct_use: |2-

      ```python
      from transformers import pipeline

      pipe = pipeline("text-classification", model="2024-mcm-everitt-ryan/flan-t5-xl-job-bias-qlora-seq2seq-cls", return_all_scores=True)

      results = pipe("Join our dynamic and fast-paced team as a Junior Marketing Specialist. We seek a tech-savvy and energetic individual who thrives in a vibrant environment. Ideal candidates are digital natives with a fresh perspective, ready to adapt quickly to new trends. You should have recent experience in social media strategies and a strong understanding of current digital marketing tools. We're looking for someone with a youthful mindset, eager to bring innovative ideas to our young and ambitious team. If you're a recent graduate or early in your career, this opportunity is perfect for you!")
      print(results)
      ```
      >> [[
      {'label': 'age', 'score': 0.9883460402488708}, 
      {'label': 'disability', 'score': 0.00787709467113018}, 
      {'label': 'feminine', 'score': 0.007224376779049635}, 
      {'label': 'general', 'score': 0.09967829287052155}, 
      {'label': 'masculine', 'score': 0.0035264550242573023}, 
      {'label': 'racial', 'score': 0.014618005603551865}, 
      {'label': 'sexuality', 'score': 0.005568435415625572}
      ]]
      
model-index:
  - name: flan-t5-xl-job-bias-qlora-seq2seq-cls
    results:
      - task:
          type: multi_label_classification
        dataset:
          name: 2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark-v2
          type: mix_human-eval_synthetic
        metrics:
          - type: loss
            value: 0.5048828125
          - type: accuracy
            value: 0.7037671232876712
          - type: f1_micro
            value: 0.7165354330708661
          - type: precision_micro
            value: 0.7222222222222222
          - type: recall_micro
            value: 0.7109375
          - type: roc_auc_micro
            value: 0.833767361111111
          - type: f1_macro
            value: 0.7300939594393451
          - type: precision_macro
            value: 0.8166695514759241
          - type: recall_macro
            value: 0.7109375
          - type: roc_auc_macro
            value: 0.8337673611111112
          - type: f1_samples
            value: 0.7418215916503589
          - type: precision_samples
            value: 0.7397260273972602
          - type: recall_samples
            value: 0.7529965753424658
          - type: roc_auc_samples
            value: 0.8542420906718853
          - type: f1_weighted
            value: 0.7300939594393452
          - type: precision_weighted
            value: 0.816669551475924
          - type: recall_weighted
            value: 0.7109375
          - type: roc_auc_weighted
            value: 0.833767361111111
          - type: runtime
            value: 88.8003
          - type: samples_per_second
            value: 6.577
          - type: steps_per_second
            value: 0.822
          - type: epoch
            value: 3

Model Card for flan-t5-xl-job-bias-qlora-seq2seq-cls

Model Details

The model is a multi-label classifier designed to detect various types of bias within job descriptions.

  • Developed by: Tristan Everitt and Paul Ryan
  • Model type: Encoder-Decoder
  • Language(s) (NLP): en
  • License: apache-2.0
  • Finetuned from model: google/flan-t5-xl

Model Sources

Uses

The primary target audience for these models are researchers dedicated to identifying biased language in job descriptions.

Out-of-Scope Use

Due to the limitations inherent in large-scale language models, they should not be utilised in applications requiring factual or accurate outputs. These models do not distinguish between fact and fiction, and implicit biases are inherently subjective.

Moreover, as language models mirror the biases present in their training data, they should not be deployed in systems that directly interact with humans unless the deployers have first conducted a thorough analysis of relevant biases for the specific use case.

Bias, Risks, and Limitations

It is imperative that all users, both direct and downstream, are aware of the risks, biases, and limitations associated with this model. Important considerations include:

  • Bias in Training Data: The model may inherit and perpetuate biases from the data it was trained on.
  • Subjectivity of Bias: Bias detection is inherently subjective, and perceptions of bias can differ across contexts and users.
  • Accuracy Concerns: The model’s outputs are not guaranteed to be true or accurate, making it unsuitable for applications that require reliable information.
  • Human Interaction Risks: When incorporated into systems that interact with humans, the model’s biases may affect interactions and decision-making, potentially leading to unintended consequences.

It is crucial for users to conduct comprehensive evaluations and consider these factors when applying the model in any context.

How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import pipeline

pipe = pipeline("text-classification", model="2024-mcm-everitt-ryan/flan-t5-xl-job-bias-qlora-seq2seq-cls", return_all_scores=True)

results = pipe("Join our dynamic and fast-paced team as a Junior Marketing Specialist. We seek a tech-savvy and energetic individual who thrives in a vibrant environment. Ideal candidates are digital natives with a fresh perspective, ready to adapt quickly to new trends. You should have recent experience in social media strategies and a strong understanding of current digital marketing tools. We're looking for someone with a youthful mindset, eager to bring innovative ideas to our young and ambitious team. If you're a recent graduate or early in your career, this opportunity is perfect for you!")
print(results)
```
>> [[
{'label': 'age', 'score': 0.9883460402488708}, 
{'label': 'disability', 'score': 0.00787709467113018}, 
{'label': 'feminine', 'score': 0.007224376779049635}, 
{'label': 'general', 'score': 0.09967829287052155}, 
{'label': 'masculine', 'score': 0.0035264550242573023}, 
{'label': 'racial', 'score': 0.014618005603551865}, 
{'label': 'sexuality', 'score': 0.005568435415625572}
]]

Training Details

Training Data

Training Hyperparameters

accelerator_config="{'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}", adafactor=false, adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08, auto_find_batch_size=false, batch_eval_metrics=false, bf16=false, bf16_full_eval=false, data_seed="None", dataloader_drop_last=false, dataloader_num_workers=0, dataloader_persistent_workers=false, dataloader_pin_memory=true, dataloader_prefetch_factor="None", ddp_backend="None", ddp_broadcast_buffers="None", ddp_bucket_cap_mb="None", ddp_find_unused_parameters="None", ddp_timeout=1800, deepspeed="None", disable_tqdm=false, dispatch_batches="None", do_eval=true, do_predict=false, do_train=false, eval_accumulation_steps="None", eval_batch_size=8, eval_delay=0, eval_do_concat_batches=true, eval_on_start=false, eval_steps="None", eval_strategy="epoch", evaluation_strategy="None", fp16=false, fp16_backend="auto", fp16_full_eval=false, fp16_opt_level="O1", fsdp="[]", fsdp_config="{'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}", fsdp_min_num_params=0, fsdp_transformer_layer_cls_to_wrap="None", full_determinism=false, generation_config="None", generation_max_length="None", generation_num_beams="None", gradient_accumulation_steps=1, gradient_checkpointing=false, gradient_checkpointing_kwargs="None", greater_is_better=false, group_by_length=false, half_precision_backend="auto", ignore_data_skip=false, include_inputs_for_metrics=false, jit_mode_eval=false, label_names="None", label_smoothing_factor=0.0, learning_rate=0.001, length_column_name="length", load_best_model_at_end=true, local_rank=0, lr_scheduler_kwargs="{}", lr_scheduler_type="linear", max_grad_norm=1.0, max_steps=-1, metric_for_best_model="loss", mp_parameters="", neftune_noise_alpha="None", no_cuda=false, num_train_epochs=3, optim="adamw_torch", optim_args="None", optim_target_modules="None", past_index=-1, per_device_eval_batch_size=8, per_device_train_batch_size=8, per_gpu_eval_batch_size="None", per_gpu_train_batch_size="None", predict_with_generate=true, prediction_loss_only=false, ray_scope="last", remove_unused_columns=true, report_to="[]", restore_callback_states_from_checkpoint=false, resume_from_checkpoint="None", seed=42, skip_memory_metrics=true, sortish_sampler=false, split_batches="None", tf32="None", torch_compile=false, torch_compile_backend="None", torch_compile_mode="None", torchdynamo="None", tpu_num_cores="None", train_batch_size=8, use_cpu=false, use_ipex=false, use_legacy_prediction_loop=false, use_mps_device=false, warmup_ratio=0.0, warmup_steps=0, weight_decay=0.001

Results

              precision    recall  f1-score   support

         age       0.89      0.59      0.71        80
  disability       0.89      0.40      0.55        80
    feminine       0.92      0.90      0.91        80
     general       0.79      0.60      0.68        80
   masculine       0.83      0.68      0.74        80
     neutral       0.37      0.93      0.53        80
      racial       0.89      0.79      0.83        80
   sexuality       0.96      0.81      0.88        80

   micro avg       0.72      0.71      0.72       640
   macro avg       0.82      0.71      0.73       640
weighted avg       0.82      0.71      0.73       640
 samples avg       0.74      0.75      0.74       640

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: 1 X NVIDIA L40S
  • Hours used: 1.47
  • Cloud Provider: N/A
  • Compute Region: N/A
  • Carbon Emitted: N/A

Compute Infrastructure

  • Linux 5.15.0-78-generic x86_64
  • MemTotal: 1056619068 kB
  • 256 X AMD EPYC 7702 64-Core Processor
  • GPU_0: NVIDIA L40S

Software

python 3.10.12, accelerate 0.32.1, aiohttp 3.9.5, aiosignal 1.3.1, anyio 4.2.0, argon2-cffi 23.1.0, argon2-cffi-bindings 21.2.0, arrow 1.3.0, asttokens 2.4.1, async-lru 2.0.4, async-timeout 4.0.3, attrs 23.2.0, awscli 1.33.26, Babel 2.14.0, beautifulsoup4 4.12.3, bitsandbytes 0.43.1, bleach 6.1.0, blinker 1.4, botocore 1.34.144, certifi 2024.2.2, cffi 1.16.0, charset-normalizer 3.3.2, click 8.1.7, cloudpickle 3.0.0, colorama 0.4.6, comm 0.2.1, cryptography 3.4.8, dask 2024.7.0, datasets 2.20.0, dbus-python 1.2.18, debugpy 1.8.0, decorator 5.1.1, defusedxml 0.7.1, dill 0.3.8, distro 1.7.0, docutils 0.16, einops 0.8.0, entrypoints 0.4, evaluate 0.4.2, exceptiongroup 1.2.0, executing 2.0.1, fastjsonschema 2.19.1, filelock 3.13.1, flash-attn 2.6.1, fqdn 1.5.1, frozenlist 1.4.1, fsspec 2024.2.0, h11 0.14.0, hf_transfer 0.1.6, httpcore 1.0.2, httplib2 0.20.2, httpx 0.26.0, huggingface-hub 0.23.4, idna 3.6, importlib_metadata 8.0.0, iniconfig 2.0.0, ipykernel 6.29.0, ipython 8.21.0, ipython-genutils 0.2.0, ipywidgets 8.1.1, isoduration 20.11.0, jedi 0.19.1, jeepney 0.7.1, Jinja2 3.1.3, jmespath 1.0.1, joblib 1.4.2, json5 0.9.14, jsonpointer 2.4, jsonschema 4.21.1, jsonschema-specifications 2023.12.1, jupyter-archive 3.4.0, jupyter_client 7.4.9, jupyter_contrib_core 0.4.2, jupyter_contrib_nbextensions 0.7.0, jupyter_core 5.7.1, jupyter-events 0.9.0, jupyter-highlight-selected-word 0.2.0, jupyter-lsp 2.2.2, jupyter-nbextensions-configurator 0.6.3, jupyter_server 2.12.5, jupyter_server_terminals 0.5.2, jupyterlab 4.1.0, jupyterlab_pygments 0.3.0, jupyterlab_server 2.25.2, jupyterlab-widgets 3.0.9, keyring 23.5.0, launchpadlib 1.10.16, lazr.restfulclient 0.14.4, lazr.uri 1.0.6, locket 1.0.0, lxml 5.1.0, MarkupSafe 2.1.5, matplotlib-inline 0.1.6, mistune 3.0.2, more-itertools 8.10.0, mpmath 1.3.0, multidict 6.0.5, multiprocess 0.70.16, nbclassic 1.0.0, nbclient 0.9.0, nbconvert 7.14.2, nbformat 5.9.2, nest-asyncio 1.6.0, networkx 3.2.1, nltk 3.8.1, notebook 6.5.5, notebook_shim 0.2.3, numpy 1.26.3, nvidia-cublas-cu12 12.1.3.1, nvidia-cuda-cupti-cu12 12.1.105, nvidia-cuda-nvrtc-cu12 12.1.105, nvidia-cuda-runtime-cu12 12.1.105, nvidia-cudnn-cu12 8.9.2.26, nvidia-cufft-cu12 11.0.2.54, nvidia-curand-cu12 10.3.2.106, nvidia-cusolver-cu12 11.4.5.107, nvidia-cusparse-cu12 12.1.0.106, nvidia-nccl-cu12 2.19.3, nvidia-nvjitlink-cu12 12.3.101, nvidia-nvtx-cu12 12.1.105, oauthlib 3.2.0, overrides 7.7.0, packaging 23.2, pandas 2.2.2, pandocfilters 1.5.1, parso 0.8.3, partd 1.4.2, peft 0.11.1, pexpect 4.9.0, pillow 10.2.0, pip 24.1.2, platformdirs 4.2.0, pluggy 1.5.0, polars 1.1.0, prometheus-client 0.19.0, prompt-toolkit 3.0.43, protobuf 5.27.2, psutil 5.9.8, ptyprocess 0.7.0, pure-eval 0.2.2, pyarrow 16.1.0, pyarrow-hotfix 0.6, pyasn1 0.6.0, pycparser 2.21, Pygments 2.17.2, PyGObject 3.42.1, PyJWT 2.3.0, pyparsing 2.4.7, pytest 8.2.2, python-apt 2.4.0+ubuntu3, python-dateutil 2.8.2, python-json-logger 2.0.7, pytz 2024.1, PyYAML 6.0.1, pyzmq 24.0.1, referencing 0.33.0, regex 2024.5.15, requests 2.32.3, rfc3339-validator 0.1.4, rfc3986-validator 0.1.1, rpds-py 0.17.1, rsa 4.7.2, s3transfer 0.10.2, safetensors 0.4.3, scikit-learn 1.5.1, scipy 1.14.0, SecretStorage 3.3.1, Send2Trash 1.8.2, sentence-transformers 3.0.1, sentencepiece 0.2.0, setuptools 69.0.3, six 1.16.0, sniffio 1.3.0, soupsieve 2.5, stack-data 0.6.3, sympy 1.12, tabulate 0.9.0, terminado 0.18.0, threadpoolctl 3.5.0, tiktoken 0.7.0, tinycss2 1.2.1, tokenizers 0.19.1, tomli 2.0.1, toolz 0.12.1, torch 2.2.0, torchaudio 2.2.0, torchdata 0.7.1, torchtext 0.17.0, torchvision 0.17.0, tornado 6.4, tqdm 4.66.4, traitlets 5.14.1, transformers 4.42.4, triton 2.2.0, types-python-dateutil 2.8.19.20240106, typing_extensions 4.9.0, tzdata 2024.1, uri-template 1.3.0, urllib3 2.2.2, wadllib 1.3.6, wcwidth 0.2.13, webcolors 1.13, webencodings 0.5.1, websocket-client 1.7.0, wheel 0.42.0, widgetsnbextension 4.0.9, xxhash 3.4.1, yarl 1.9.4, zipp 1.0.0

Citation

BibTeX:

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