Instructions to use STUTI12345/scibert_epd_5tag_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use STUTI12345/scibert_epd_5tag_lora with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("allenai/scibert_scivocab_uncased") model = PeftModel.from_pretrained(base_model, "STUTI12345/scibert_epd_5tag_lora") - Transformers
How to use STUTI12345/scibert_epd_5tag_lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="STUTI12345/scibert_epd_5tag_lora")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("STUTI12345/scibert_epd_5tag_lora") model = AutoModelForTokenClassification.from_pretrained("STUTI12345/scibert_epd_5tag_lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
scibert_epd_5tag_lora
This model is a fine-tuned version of allenai/scibert_scivocab_uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0012
- Precision: 0.9995
- Recall: 0.9983
- F1: 0.9989
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- 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: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| 0.0013 | 1.0 | 7292 | 0.0018 | 0.9977 | 0.9984 | 0.9980 |
| 0.0003 | 2.0 | 14584 | 0.0011 | 0.9992 | 0.9988 | 0.9990 |
| 0.0044 | 3.0 | 21876 | 0.0010 | 0.9994 | 0.9991 | 0.9992 |
Framework versions
- PEFT 0.18.0
- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1
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Model tree for STUTI12345/scibert_epd_5tag_lora
Base model
allenai/scibert_scivocab_uncased
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("allenai/scibert_scivocab_uncased") model = PeftModel.from_pretrained(base_model, "STUTI12345/scibert_epd_5tag_lora")