Instructions to use rseeto/multilabel_disease_bert_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rseeto/multilabel_disease_bert_classification with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased") model = PeftModel.from_pretrained(base_model, "rseeto/multilabel_disease_bert_classification") - Transformers
How to use rseeto/multilabel_disease_bert_classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rseeto/multilabel_disease_bert_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from rseeto/multilabel_disease_bert_classification: direct link, hf CLI and curl.
- Browser
- Download file 1.79 kB
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https://huggingface.co/rseeto/multilabel_disease_bert_classification/resolve/main/README.md
- Command line
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hf download hf://rseeto/multilabel_disease_bert_classification/README.md
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curl -L -o README.md https://huggingface.co/rseeto/multilabel_disease_bert_classification/resolve/main/README.md
1.79 kB
| library_name: peft | |
| license: apache-2.0 | |
| base_model: bert-base-uncased | |
| tags: | |
| - base_model:adapter:bert-base-uncased | |
| - lora | |
| - transformers | |
| model-index: | |
| - name: bert-base-uncased | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # bert-base-uncased | |
| This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: nan | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 5e-05 | |
| - train_batch_size: 16 | |
| - eval_batch_size: 16 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - num_epochs: 8 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | No log | 1.0 | 60 | nan | | |
| | 0.3696 | 2.0 | 120 | nan | | |
| | 0.3696 | 3.0 | 180 | nan | | |
| | 0.1117 | 4.0 | 240 | nan | | |
| | 0.0942 | 5.0 | 300 | nan | | |
| | 0.0942 | 6.0 | 360 | nan | | |
| | 0.0894 | 7.0 | 420 | nan | | |
| | 0.0894 | 8.0 | 480 | nan | | |
| ### Framework versions | |
| - PEFT 0.18.0 | |
| - Transformers 4.57.2 | |
| - Pytorch 2.9.0+cu126 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.22.1 |