---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-cased-emotion
  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-cased-emotion

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co./distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1771
- Accuracy: 0.9265
- F1: 0.9263
- Precision: 0.9276
- Recall: 0.9265

## 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: 32
- eval_batch_size: 32
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2633        | 1.0   | 500  | 0.2505          | 0.917    | 0.9174 | 0.9183    | 0.917  |
| 0.1815        | 2.0   | 1000 | 0.1921          | 0.9305   | 0.9304 | 0.9329    | 0.9305 |
| 0.1224        | 3.0   | 1500 | 0.1721          | 0.9355   | 0.9361 | 0.9388    | 0.9355 |
| 0.093         | 4.0   | 2000 | 0.1712          | 0.9365   | 0.9359 | 0.9367    | 0.9365 |
| 0.0782        | 5.0   | 2500 | 0.2116          | 0.9275   | 0.9271 | 0.9272    | 0.9275 |
| 0.0548        | 6.0   | 3000 | 0.2353          | 0.936    | 0.9348 | 0.9362    | 0.936  |
| 0.0358        | 7.0   | 3500 | 0.2729          | 0.9325   | 0.9331 | 0.9345    | 0.9325 |
| 0.0185        | 8.0   | 4000 | 0.3059          | 0.9325   | 0.9323 | 0.9322    | 0.9325 |
| 0.0124        | 9.0   | 4500 | 0.3103          | 0.9325   | 0.9325 | 0.9325    | 0.9325 |
| 0.0137        | 10.0  | 5000 | 0.3161          | 0.9305   | 0.9303 | 0.9303    | 0.9305 |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6