Model: 2024-mcm-everitt-ryan/Phi-3-mini-4k-instruct-job-bias-qlora-seq-cls
Author: 2024-mcm-everitt-ryan
Pipeline: text-classification
Library: transformers
Tags: transformers, safetensors, phi3, text-classification, custom_code, en, dataset:2024-mcm-everitt-ryan/job-bias-synthetic-human-benchmark-v2, arxiv:1910.09700, base_model:microsoft/Phi-3-mini-4k-instruct, base_model:quantized:microsoft/Phi-3-mini-4k-instruct, license:apache-2.0, model-index, text-generation-inference, endpoints_compatible, 4-bit, bitsandbytes, region:us
Base models (quantized): id: microsoft/Phi-3-mini-4k-instruct
Card metadata: 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
    
             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
    
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: 
    ```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}
    ]]
    
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
verified: no, type: accuracy
value: 0.6078767123287672
verified: no, type: f1_micro
value: 0.6507936507936508
verified: no, type: precision_micro
value: 0.6612903225806451
verified: no, type: recall_micro
value: 0.640625
verified: no, type: roc_auc_micro
value: 0.7942708333333334
verified: no, type: f1_macro
value: 0.6759919550021907
verified: no, type: precision_macro
value: 0.8274147252372333
verified: no, type: recall_macro
value: 0.640625
verified: no, type: roc_auc_macro
value: 0.7942708333333334
verified: no, type: f1_samples
value: 0.6690639269406393
verified: no, type: precision_samples
value: 0.661244292237443
verified: no, type: recall_samples
value: 0.6936358447488585
verified: no, type: roc_auc_samples
value: 0.8201993639921722
verified: no, type: f1_weighted
value: 0.6759919550021907
verified: no, type: precision_weighted
value: 0.8274147252372334
verified: no, type: recall_weighted
value: 0.640625
verified: no, type: roc_auc_weighted
value: 0.7942708333333334
verified: no, type: runtime
value: 109.3748
verified: no, type: samples_per_second
value: 5.339
verified: no, type: steps_per_second
value: 0.667
verified: no, type: epoch
value: 3
verified: no
Likes: 0 | All-time downloads: 332
Created: 2024-07-17 15:21:03