---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sst2-midterm
  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-sst2-midterm

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: 0.3341
- Accuracy: 0.9048
- F1: 0.9048
- F1 0 Class: 0.9022
- F1 1 Class: 0.9073
- Precision 0 Class: 0.9097
- Precision 1 Class: 0.9002
- Recall 0 Class: 0.8949
- Recall 1 Class: 0.9144

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | F1 0 Class | F1 1 Class | Precision 0 Class | Precision 1 Class | Recall 0 Class | Recall 1 Class |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:----------:|:----------:|:-----------------:|:-----------------:|:--------------:|:--------------:|
| 0.4244        | 1.0   | 109  | 0.2882          | 0.8842   | 0.8841 | 0.8790     | 0.8889     | 0.9017            | 0.8688            | 0.8575         | 0.9099         |
| 0.2289        | 2.0   | 218  | 0.2641          | 0.8922   | 0.8922 | 0.8889     | 0.8953     | 0.8995            | 0.8855            | 0.8785         | 0.9054         |
| 0.1517        | 3.0   | 327  | 0.3000          | 0.8933   | 0.8934 | 0.8930     | 0.8937     | 0.8798            | 0.9072            | 0.9065         | 0.8806         |
| 0.1048        | 4.0   | 436  | 0.3217          | 0.8991   | 0.8990 | 0.8952     | 0.9027     | 0.9126            | 0.8870            | 0.8785         | 0.9189         |
| 0.0767        | 5.0   | 545  | 0.3341          | 0.9048   | 0.9048 | 0.9022     | 0.9073     | 0.9097            | 0.9002            | 0.8949         | 0.9144         |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2