README.md

---
base_model: microsoft/Phi-3-mini-4k-instruct
datasets:
- 2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark-v2
language: en
license: apache-2.0
model_id: Phi-3-mini-4k-instruct-job-bias-qlora-seq-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,
  gradient_accumulation_steps=1, gradient_checkpointing="(False,)", gradient_checkpointing_kwargs="None",
  greater_is_better=false, group_by_length=true, 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=0.0001, 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", 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, 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\n    \n      \
  \       age       0.89      0.40      0.55        80\n      disability       0.97\
  \      0.44      0.60        80\n        feminine       0.99      0.89      0.93\
  \        80\n         general       0.65      0.51      0.57        80\n       masculine\
  \       0.95      0.45      0.61        80\n         neutral       0.30      0.90\
  \      0.44        80\n          racial       0.93      0.79      0.85        80\n\
  \       sexuality       0.95      0.75      0.84        80\n    \n       micro avg\
  \       0.66      0.64      0.65       640\n       macro avg       0.83      0.64\
  \      0.68       640\n    weighted avg       0.83      0.64      0.68       640\n\
  \     samples avg       0.66      0.69      0.67       640\n    "
compute_infrastructure: '- Linux 6.5.0-28-generic x86_64

  - MemTotal:       527988292 kB

  - 64 X Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz

  - 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: '1.82'
cloud_provider: N/A
cloud_region: N/A
co2_emitted: N/A
direct_use: "\n    ```python\n    from transformers import pipeline\n\n    pipe =\
  \ pipeline(\"text-classification\", model=\"2024-mcm-everitt-ryan/Phi-3-mini-4k-instruct-job-bias-qlora-seq-cls\"\
  , return_all_scores=True)\n\n    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!\")\n    print(results)\n\
  \    ```\n    >> [[\n    {'label': 'age', 'score': 0.9883460402488708}, \n    {'label':\
  \ 'disability', 'score': 0.00787709467113018}, \n    {'label': 'feminine', 'score':\
  \ 0.007224376779049635}, \n    {'label': 'general', 'score': 0.09967829287052155},\
  \ \n    {'label': 'masculine', 'score': 0.0035264550242573023}, \n    {'label':\
  \ 'racial', 'score': 0.014618005603551865}, \n    {'label': 'sexuality', 'score':\
  \ 0.005568435415625572}\n    ]]\n    "
model-index:
- name: Phi-3-mini-4k-instruct-job-bias-qlora-seq-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: 0.3098137676715851
    - type: accuracy
      value: 0.6078767123287672
    - type: f1_micro
      value: 0.6507936507936508
    - type: precision_micro
      value: 0.6612903225806451
    - type: recall_micro
      value: 0.640625
    - type: roc_auc_micro
      value: 0.7942708333333334
    - type: f1_macro
      value: 0.6759919550021907
    - type: precision_macro
      value: 0.8274147252372333
    - type: recall_macro
      value: 0.640625
    - type: roc_auc_macro
      value: 0.7942708333333334
    - type: f1_samples
      value: 0.6690639269406393
    - type: precision_samples
      value: 0.661244292237443
    - type: recall_samples
      value: 0.6936358447488585
    - type: roc_auc_samples
      value: 0.8201993639921722
    - type: f1_weighted
      value: 0.6759919550021907
    - type: precision_weighted
      value: 0.8274147252372334
    - type: recall_weighted
      value: 0.640625
    - type: roc_auc_weighted
      value: 0.7942708333333334
    - type: runtime
      value: 109.3748
    - type: samples_per_second
      value: 5.339
    - type: steps_per_second
      value: 0.667
    - type: epoch
      value: 3.0
---

# Model Card for Phi-3-mini-4k-instruct-job-bias-qlora-seq-cls

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

<!-- Provide a longer summary of what this model is. -->

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:** Decoder
- **Language(s) (NLP):** en
- **License:** apache-2.0
- **Finetuned from model:** microsoft/Phi-3-mini-4k-instruct

### Model Sources

- **Repository:** https://github.com/2024-mcm-everitt-ryan
- **Paper:** In Progress

## 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/Phi-3-mini-4k-instruct-job-bias-qlora-seq-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

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

- [2024-mcm-everitt-ryan/benchmark](https://huggingface.co/datasets/2024-mcm-everitt-ryan/benchmark)


#### 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, gradient_accumulation_steps=1, gradient_checkpointing="(False,)", gradient_checkpointing_kwargs="None", greater_is_better=false, group_by_length=true, 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=0.0001, 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", 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, 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 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->



## Results

                  precision    recall  f1-score   support
    
             age       0.89      0.40      0.55        80
      disability       0.97      0.44      0.60        80
        feminine       0.99      0.89      0.93        80
         general       0.65      0.51      0.57        80
       masculine       0.95      0.45      0.61        80
         neutral       0.30      0.90      0.44        80
          racial       0.93      0.79      0.85        80
       sexuality       0.95      0.75      0.84        80
    
       micro avg       0.66      0.64      0.65       640
       macro avg       0.83      0.64      0.68       640
    weighted avg       0.83      0.64      0.68       640
     samples avg       0.66      0.69      0.67       640
    
[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** 1 X NVIDIA L40S
- **Hours used:** 1.82
- **Cloud Provider:** N/A
- **Compute Region:** N/A
- **Carbon Emitted:** N/A

### Compute Infrastructure

- Linux 6.5.0-28-generic x86_64
- MemTotal:       527988292 kB
- 64 X Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
- 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

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**BibTeX:**

In Progress