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README.md

@@ -1,9 +1,187 @@
 ---
-library_name: transformers
-tags: []
+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: "                  precision    recall  f1-score   support\n    \n      \
+  \       age       0.89      0.59      0.71        80\n      disability       0.89\
+  \      0.40      0.55        80\n        feminine       0.92      0.90      0.91\
+  \        80\n         general       0.79      0.60      0.68        80\n       masculine\
+  \       0.83      0.68      0.74        80\n         neutral       0.37      0.93\
+  \      0.53        80\n          racial       0.89      0.79      0.83        80\n\
+  \       sexuality       0.96      0.81      0.88        80\n    \n       micro avg\
+  \       0.72      0.71      0.72       640\n       macro avg       0.82      0.71\
+  \      0.73       640\n    weighted avg       0.82      0.71      0.73       640\n\
+  \     samples avg       0.74      0.75      0.74       640\n    "
+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: "\n    ```python\n    from transformers import pipeline\n\n    pipe =\
+  \ pipeline(\"text-classification\", model=\"2024-mcm-everitt-ryan/flan-t5-xl-job-bias-qlora-seq2seq-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: 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: 3.0
 ---
 
-# Model Card for Model ID
+# Model Card for flan-t5-xl-job-bias-qlora-seq2seq-cls
 
 <!-- Provide a quick summary of what the model is/does. -->
 
@@ -15,21 +193,21 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+The model is a multi-label classifier designed to detect various types of bias within job descriptions.
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** Tristan Everitt and Paul Ryan
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Language(s) (NLP):** en
+- **License:** apache-2.0
+- **Finetuned from model [optional]:** google/flan-t5-xl
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
+- **Repository:** https://gitlab.computing.dcu.ie/everitt2/2024-mcm-everitt-ryan
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
@@ -41,7 +219,25 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+
+    ```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}
+    ]]
+    
 
 ### Downstream Use [optional]
 
@@ -92,7 +288,7 @@ Use the code below to get started with the model.
 
 #### Training Hyperparameters
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **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 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 #### Speeds, Sizes, Times [optional]
 
@@ -126,7 +322,22 @@ Use the code below to get started with the model.
 
 ### Results
 
-[More Information Needed]
+                  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
+    
 
 #### Summary
 
@@ -144,11 +355,11 @@ Use the code below to get started with the model.
 
 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:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Hardware Type:** 1 X NVIDIA L40S
+- **Hours used:** 1.47
+- **Cloud Provider:** N/A
+- **Compute Region:** N/A
+- **Carbon Emitted:** N/A
 
 ## Technical Specifications [optional]
 
@@ -158,7 +369,10 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Compute Infrastructure
 
-[More Information Needed]
+- Linux 5.15.0-78-generic x86_64
+- MemTotal:       1056619068 kB
+- 256 X AMD EPYC 7702 64-Core Processor
+- GPU_0: NVIDIA L40S
 
 #### Hardware
 
@@ -166,7 +380,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Software
 
-[More Information Needed]
+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
 
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@@ -192,8 +406,8 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
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