grazh/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext-finetuned-ner
By grazh · ↓ 3 · ♥ 0 · Updated 2025-11-23
What's in this repo
This page lists the files in grazh/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext-finetuned-ner, including model.onnx, model_bnb4.onnx, model_fp16.onnx and more.
- 8 ONNX models (.onnx files)
- 20 files, including 8 .onnx, 5 .json, 1 .md, 1 .safetensors, 1 .3, 1 .4
- Last updated 2025-11-23
ONNX files
| File | Size | Download |
|---|---|---|
| onnx/model.onnx | — | Download |
| onnx/model_bnb4.onnx | — | Download |
| onnx/model_fp16.onnx | — | Download |
| onnx/model_int8.onnx | — | Download |
| onnx/model_q4.onnx | — | Download |
| onnx/model_q4f16.onnx | — | Download |
| onnx/model_quantized.onnx | — | Download |
| onnx/model_uint8.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext-finetuned-ner
This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1194
- Precision: 0.7245
- Recall: 0.8681
- F1: 0.7898
- Accuracy: 0.9547
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: 2e-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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 364 | 0.1323 | 0.6965 | 0.8613 | 0.7702 | 0.9515 |
| 0.212 | 2.0 | 728 | 0.1168 | 0.7195 | 0.8646 | 0.7854 | 0.9538 |
| 0.1015 | 3.0 | 1092 | 0.1194 | 0.7245 | 0.8681 | 0.7898 | 0.9547 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.1
Model info
Created: 2025-11-22
Updated: 2025-11-23
View on Hugging Face