---

model-index:
- name: DracoHugging/Distilbert-sentiment-analysis
  results:
  - task:
      type: Text Classification             # Required. Example: automatic-speech-recognition
      name: Sentiment Analysis            # Optional. Example: Speech Recognition

    dataset:
      type: Text-2-Text          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
      name: knkarthick/dialogsum    # Required. A pretty name for the dataset. Example: Common Voice (French)

    metrics:
      - type: Validation Loss        # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 1.08      # Required. Example: 20.90
        verified: true 
---

<!-- 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-sentiment-analysis

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: 1.2745

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1633        | 1.0   | 1178 | 1.1116          |
| 1.0524        | 2.0   | 2356 | 1.0836          |
| 0.9103        | 3.0   | 3534 | 1.1135          |
| 0.7676        | 4.0   | 4712 | 1.1945          |
| 0.659         | 5.0   | 5890 | 1.2745          |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3