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
metadata
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: []
bert-base-uncased
This model is a fine-tuned version of 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