---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ellis-v2-emotion-leadership-multi-label
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ellis-v2-emotion-leadership-multi-label

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1172
- Accuracy: 0.9690
- F1: 0.9224
- Precision: 0.9250
- Recall: 0.9198

## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.125         | 1.0   | 5910  | 0.1381          | 0.9607   | 0.9011 | 0.9069    | 0.8954 |
| 0.1043        | 2.0   | 11820 | 0.1172          | 0.9690   | 0.9224 | 0.9250    | 0.9198 |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1