---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: recitation-segmenter-v2
  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. -->

# recitation-segmenter-v2

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9958
- F1: 0.9964
- Loss: 0.0132
- Precision: 0.9976
- Recall: 0.9951

## 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: 50
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Accuracy | F1     | Validation Loss | Precision | Recall |
|:-------------:|:------:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.0701        | 0.2507 | 275  | 0.9953   | 0.9959 | 0.0249          | 0.9947    | 0.9971 |
| 0.0234        | 0.5014 | 550  | 0.9953   | 0.9959 | 0.0185          | 0.9940    | 0.9977 |
| 0.0186        | 0.7521 | 825  | 0.9958   | 0.9964 | 0.0132          | 0.9976    | 0.9951 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.2.1+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1