---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: wavlm-large-finetuned-iemocap
  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. -->

# wavlm-large-finetuned-iemocap

This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1588
- Accuracy: 0.4811
- F1: 0.4602

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.3733        | 0.98  | 25   | 1.3723          | 0.2502   | 0.1002 |
| 1.2784        | 1.98  | 50   | 1.3130          | 0.3307   | 0.2503 |
| 1.2228        | 2.98  | 75   | 1.2485          | 0.3899   | 0.3398 |
| 1.1588        | 3.98  | 100  | 1.2129          | 0.4646   | 0.4650 |
| 1.1116        | 4.98  | 125  | 1.1941          | 0.4753   | 0.4655 |
| 1.1212        | 5.98  | 150  | 1.1688          | 0.4762   | 0.4639 |
| 1.0919        | 6.98  | 175  | 1.1574          | 0.4850   | 0.4710 |
| 1.0749        | 7.98  | 200  | 1.1612          | 0.4840   | 0.4639 |
| 1.0943        | 8.98  | 225  | 1.1586          | 0.4888   | 0.4677 |
| 1.0746        | 9.98  | 250  | 1.1588          | 0.4811   | 0.4602 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2