---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: patentClassfication2
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# patentClassfication2

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: 0.6263
- Accuracy: 0.6572
- F1: 0.6151
- Precision: 0.6966
- Recall: 0.5507

## 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: 2.54241e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 41
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 24
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6346        | 1.0   | 4438  | 0.6263          | 0.6572   | 0.6151 | 0.6966    | 0.5507 |
| 0.5796        | 2.0   | 8876  | 0.6388          | 0.6758   | 0.6833 | 0.6646    | 0.7030 |
| 0.5268        | 3.0   | 13314 | 0.6567          | 0.6715   | 0.6833 | 0.6565    | 0.7123 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.14.4
- Tokenizers 0.13.3