---
license: apache-2.0
base_model: facebook/wav2vec2-conformer-rope-large-960h-ft
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: humor_model_conformer
  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. -->

# humor_model_conformer

This model is a fine-tuned version of [facebook/wav2vec2-conformer-rope-large-960h-ft](https://huggingface.co/facebook/wav2vec2-conformer-rope-large-960h-ft) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3082
- Accuracy: 0.9277

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.9524 | 5    | 0.5816          | 0.8434   |
| 0.5426        | 1.9048 | 10   | 0.4556          | 0.9337   |
| 0.5426        | 2.8571 | 15   | 0.3911          | 0.9398   |
| 0.4332        | 4.0    | 21   | 0.3451          | 0.9518   |
| 0.4332        | 4.9524 | 26   | 0.3284          | 0.9398   |
| 0.3777        | 5.9048 | 31   | 0.3176          | 0.9398   |
| 0.3777        | 6.8571 | 36   | 0.3116          | 0.9398   |
| 0.3709        | 8.0    | 42   | 0.3087          | 0.9277   |
| 0.3709        | 8.9524 | 47   | 0.3140          | 0.9337   |
| 0.3545        | 9.5238 | 50   | 0.3082          | 0.9277   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1