---
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
datasets:
- scicite
metrics:
- accuracy
model-index:
- name: Scicite_classification_model
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: scicite
      type: scicite
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8493449781659389
---

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

This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the scicite dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2264
- Accuracy: 0.8493

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3988        | 1.0   | 513  | 0.4386          | 0.8428   |
| 0.2701        | 2.0   | 1026 | 0.5087          | 0.8504   |
| 0.1789        | 3.0   | 1539 | 0.6278          | 0.8526   |
| 0.1047        | 4.0   | 2052 | 0.8142          | 0.8548   |
| 0.0653        | 5.0   | 2565 | 1.0136          | 0.8461   |
| 0.0376        | 6.0   | 3078 | 1.1257          | 0.8417   |
| 0.0237        | 7.0   | 3591 | 1.1632          | 0.8341   |
| 0.0135        | 8.0   | 4104 | 1.2346          | 0.8493   |
| 0.0124        | 9.0   | 4617 | 1.2027          | 0.8483   |
| 0.0059        | 10.0  | 5130 | 1.2264          | 0.8493   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3