base_model: google/gemma-2-9b
datasets:
- 2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark-v2
language: en
license: apache-2.0
model_id: gemma-2-9b-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: |2-
precision recall f1-score support
age 0.72 0.60 0.65 80
disability 0.95 0.50 0.66 80
feminine 0.99 0.91 0.95 80
general 0.84 0.46 0.60 80
masculine 0.65 0.66 0.66 80
neutral 0.34 0.86 0.49 80
racial 0.90 0.82 0.86 80
sexuality 0.97 0.75 0.85 80
micro avg 0.69 0.70 0.69 640
macro avg 0.80 0.70 0.71 640
weighted avg 0.80 0.70 0.71 640
samples avg 0.71 0.74 0.72 640
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: '4.18'
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/gemma-2-9b-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}
]]
model-index:
- name: gemma-2-9b-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: 0.2838529944419861
- type: accuracy
value: 0.6438356164383562
- type: f1_micro
value: 0.6947040498442367
- type: precision_micro
value: 0.6925465838509317
- type: recall_micro
value: 0.696875
- type: roc_auc_micro
value: 0.8238839285714286
- type: f1_macro
value: 0.7138661109496585
- type: precision_macro
value: 0.7957063168116414
- type: recall_macro
value: 0.6968749999999999
- type: roc_auc_macro
value: 0.8238839285714286
- type: f1_samples
value: 0.7163078930202218
- type: precision_samples
value: 0.7057648401826483
- type: recall_samples
value: 0.7441495433789955
- type: roc_auc_samples
value: 0.8469646934116113
- type: f1_weighted
value: 0.7138661109496585
- type: precision_weighted
value: 0.7957063168116415
- type: recall_weighted
value: 0.696875
- type: roc_auc_weighted
value: 0.8238839285714284
- type: runtime
value: 251.9003
- type: samples_per_second
value: 2.318
- type: steps_per_second
value: 0.29
- type: epoch
value: 3
Model Card for gemma-2-9b-job-bias-qlora-seq-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: Decoder
- Language(s) (NLP): en
- License: apache-2.0
- Finetuned from model: google/gemma-2-9b
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/gemma-2-9b-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
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
Results
precision recall f1-score support
age 0.72 0.60 0.65 80
disability 0.95 0.50 0.66 80
feminine 0.99 0.91 0.95 80
general 0.84 0.46 0.60 80
masculine 0.65 0.66 0.66 80
neutral 0.34 0.86 0.49 80
racial 0.90 0.82 0.86 80
sexuality 0.97 0.75 0.85 80
micro avg 0.69 0.70 0.69 640
macro avg 0.80 0.70 0.71 640
weighted avg 0.80 0.70 0.71 640
samples avg 0.71 0.74 0.72 640
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: 4.18
- 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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