|
--- |
|
base_model: allenai/scibert_scivocab_uncased |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: CRAFT_SciBERT_NER |
|
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. --> |
|
|
|
# CRAFT_SciBERT_NER |
|
|
|
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1143 |
|
- Seqeval classification report: precision recall f1-score support |
|
|
|
CHEBI 0.74 0.70 0.72 457 |
|
CL 0.82 0.75 0.78 1099 |
|
GGP 0.92 0.93 0.93 2232 |
|
GO 0.78 0.84 0.81 2508 |
|
SO 0.83 0.81 0.82 1365 |
|
Taxon 0.99 0.99 0.99 87655 |
|
|
|
micro avg 0.98 0.98 0.98 95316 |
|
macro avg 0.85 0.84 0.84 95316 |
|
weighted avg 0.98 0.98 0.98 95316 |
|
|
|
|
|
## 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 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
|
| No log | 1.0 | 347 | 0.1140 | precision recall f1-score support |
|
|
|
CHEBI 0.66 0.69 0.67 457 |
|
CL 0.83 0.69 0.75 1099 |
|
GGP 0.89 0.93 0.91 2232 |
|
GO 0.76 0.85 0.80 2508 |
|
SO 0.79 0.73 0.76 1365 |
|
Taxon 0.99 0.99 0.99 87655 |
|
|
|
micro avg 0.97 0.97 0.97 95316 |
|
macro avg 0.82 0.81 0.81 95316 |
|
weighted avg 0.97 0.97 0.97 95316 |
|
| |
|
| 0.1263 | 2.0 | 695 | 0.1126 | precision recall f1-score support |
|
|
|
CHEBI 0.73 0.69 0.71 457 |
|
CL 0.85 0.72 0.78 1099 |
|
GGP 0.91 0.93 0.92 2232 |
|
GO 0.74 0.87 0.80 2508 |
|
SO 0.82 0.80 0.81 1365 |
|
Taxon 0.99 0.99 0.99 87655 |
|
|
|
micro avg 0.97 0.97 0.97 95316 |
|
macro avg 0.84 0.83 0.83 95316 |
|
weighted avg 0.97 0.97 0.97 95316 |
|
| |
|
| 0.0326 | 3.0 | 1041 | 0.1143 | precision recall f1-score support |
|
|
|
CHEBI 0.74 0.70 0.72 457 |
|
CL 0.82 0.75 0.78 1099 |
|
GGP 0.92 0.93 0.93 2232 |
|
GO 0.78 0.84 0.81 2508 |
|
SO 0.83 0.81 0.82 1365 |
|
Taxon 0.99 0.99 0.99 87655 |
|
|
|
micro avg 0.98 0.98 0.98 95316 |
|
macro avg 0.85 0.84 0.84 95316 |
|
weighted avg 0.98 0.98 0.98 95316 |
|
| |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.35.2 |
|
- Pytorch 2.1.0+cu121 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
|
|