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---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: SciBERT_20K_steps
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. -->
# SciBERT_20K_steps
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: 0.0176
- Accuracy: 0.9941
- Precision: 0.7976
- Recall: 0.5324
- F1: 0.6386
- Hamming: 0.0059
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Hamming |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 0.31 | 0.02 | 1000 | 0.0780 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 |
| 0.0581 | 0.03 | 2000 | 0.0503 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 |
| 0.0488 | 0.05 | 3000 | 0.0459 | 0.9902 | 0.0 | 0.0 | 0.0 | 0.0098 |
| 0.0407 | 0.06 | 4000 | 0.0355 | 0.9911 | 0.8475 | 0.1130 | 0.1994 | 0.0089 |
| 0.0323 | 0.08 | 5000 | 0.0293 | 0.9918 | 0.8327 | 0.1961 | 0.3175 | 0.0082 |
| 0.0278 | 0.09 | 6000 | 0.0255 | 0.9924 | 0.8223 | 0.2846 | 0.4228 | 0.0076 |
| 0.0246 | 0.11 | 7000 | 0.0235 | 0.9929 | 0.8002 | 0.3609 | 0.4974 | 0.0071 |
| 0.0231 | 0.13 | 8000 | 0.0218 | 0.9933 | 0.7988 | 0.4189 | 0.5496 | 0.0067 |
| 0.0217 | 0.14 | 9000 | 0.0209 | 0.9934 | 0.7888 | 0.4444 | 0.5685 | 0.0066 |
| 0.0208 | 0.16 | 10000 | 0.0201 | 0.9935 | 0.8036 | 0.4473 | 0.5747 | 0.0065 |
| 0.0199 | 0.17 | 11000 | 0.0195 | 0.9936 | 0.7901 | 0.4751 | 0.5934 | 0.0064 |
| 0.0195 | 0.19 | 12000 | 0.0192 | 0.9938 | 0.7889 | 0.4923 | 0.6063 | 0.0062 |
| 0.019 | 0.21 | 13000 | 0.0188 | 0.9938 | 0.7999 | 0.4913 | 0.6087 | 0.0062 |
| 0.0189 | 0.22 | 14000 | 0.0184 | 0.9939 | 0.7872 | 0.5094 | 0.6186 | 0.0061 |
| 0.0188 | 0.24 | 15000 | 0.0182 | 0.9939 | 0.7920 | 0.5084 | 0.6193 | 0.0061 |
| 0.0183 | 0.25 | 16000 | 0.0180 | 0.9940 | 0.7901 | 0.5241 | 0.6301 | 0.0060 |
| 0.0181 | 0.27 | 17000 | 0.0179 | 0.9940 | 0.7897 | 0.5277 | 0.6327 | 0.0060 |
| 0.0179 | 0.28 | 18000 | 0.0177 | 0.9941 | 0.7928 | 0.5301 | 0.6353 | 0.0059 |
| 0.0179 | 0.3 | 19000 | 0.0176 | 0.9941 | 0.7953 | 0.5275 | 0.6343 | 0.0059 |
| 0.0178 | 0.32 | 20000 | 0.0176 | 0.9941 | 0.7976 | 0.5324 | 0.6386 | 0.0059 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.7.1
- Tokenizers 0.14.1
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