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---
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
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