---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-scientific-eval
  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. -->

# distilbert-base-uncased-finetuned-scientific-eval

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1810
- Precision: 0.5820
- Recall: 0.6719
- F1: 0.6237
- Accuracy: 0.9483

## 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 | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 71   | 0.3580          | 0.3871    | 0.1893 | 0.2542 | 0.9068   |
| No log        | 2.0   | 142  | 0.2235          | 0.5585    | 0.4669 | 0.5086 | 0.9332   |
| No log        | 3.0   | 213  | 0.1870          | 0.6355    | 0.5994 | 0.6169 | 0.9456   |
| No log        | 4.0   | 284  | 0.1857          | 0.5915    | 0.6120 | 0.6016 | 0.9474   |
| No log        | 5.0   | 355  | 0.1810          | 0.5820    | 0.6719 | 0.6237 | 0.9483   |
| No log        | 6.0   | 426  | 0.1969          | 0.6108    | 0.6435 | 0.6267 | 0.9505   |
| No log        | 7.0   | 497  | 0.1914          | 0.5965    | 0.6530 | 0.6235 | 0.9513   |
| 0.1851        | 8.0   | 568  | 0.1964          | 0.6040    | 0.6593 | 0.6305 | 0.9517   |
| 0.1851        | 9.0   | 639  | 0.2026          | 0.6023    | 0.6593 | 0.6295 | 0.9517   |
| 0.1851        | 10.0  | 710  | 0.2027          | 0.6011    | 0.6751 | 0.6360 | 0.9508   |


### Framework versions

- Transformers 4.27.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3