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---
base_model: allenai/scibert_scivocab_cased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: SOMD-scibert-stage1-v1
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. -->
# SOMD-scibert-stage1-v1
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- F1: 1.0
## 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: 5e-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
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| No log | 1.0 | 1243 | 0.0019 | 0.9022 |
| No log | 2.0 | 2486 | 0.0007 | 0.9530 |
| No log | 3.0 | 3729 | 0.0006 | 0.9749 |
| No log | 4.0 | 4972 | 0.0006 | 0.9697 |
| No log | 5.0 | 6215 | 0.0003 | 0.9830 |
| No log | 6.0 | 7458 | 0.0007 | 0.9759 |
| No log | 7.0 | 8701 | 0.0003 | 0.9867 |
| No log | 8.0 | 9944 | 0.0002 | 0.9900 |
| No log | 9.0 | 11187 | 0.0001 | 0.9932 |
| No log | 10.0 | 12430 | 0.0002 | 0.9951 |
| No log | 11.0 | 13673 | 0.0001 | 0.9932 |
| No log | 12.0 | 14916 | 0.0000 | 0.9978 |
| No log | 13.0 | 16159 | 0.0000 | 0.9977 |
| No log | 14.0 | 17402 | 0.0000 | 0.9985 |
| No log | 15.0 | 18645 | 0.0000 | 0.9994 |
| No log | 16.0 | 19888 | 0.0000 | 0.9992 |
| No log | 17.0 | 21131 | 0.0000 | 0.9997 |
| No log | 18.0 | 22374 | 0.0000 | 1.0 |
| No log | 19.0 | 23617 | 0.0000 | 1.0 |
| No log | 20.0 | 24860 | 0.0000 | 1.0 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
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