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# disfluency-large-2

This model is a fine-tuned version of [vinai/phobert-large](https://huggingface.co/vinai/phobert-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0318
- Precision: 0.9837
- Recall: 0.9808
- F1: 0.9822
- Accuracy: 0.9946

## 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
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 140  | 0.0439          | 0.9538    | 0.9561 | 0.9550 | 0.9890   |
| No log        | 2.0   | 280  | 0.0314          | 0.9660    | 0.9736 | 0.9698 | 0.9906   |
| No log        | 3.0   | 420  | 0.0394          | 0.9710    | 0.9651 | 0.9681 | 0.9909   |
| 0.1105        | 4.0   | 560  | 0.0320          | 0.9795    | 0.9784 | 0.9790 | 0.9929   |
| 0.1105        | 5.0   | 700  | 0.0450          | 0.9704    | 0.9657 | 0.9681 | 0.9904   |
| 0.1105        | 6.0   | 840  | 0.0463          | 0.9776    | 0.9694 | 0.9734 | 0.9911   |
| 0.1105        | 7.0   | 980  | 0.0480          | 0.9706    | 0.9712 | 0.9709 | 0.9909   |
| 0.0113        | 8.0   | 1120 | 0.0318          | 0.9837    | 0.9808 | 0.9822 | 0.9946   |
| 0.0113        | 9.0   | 1260 | 0.0419          | 0.9699    | 0.9669 | 0.9684 | 0.9915   |
| 0.0113        | 10.0  | 1400 | 0.0458          | 0.9735    | 0.9712 | 0.9723 | 0.9920   |
| 0.0051        | 11.0  | 1540 | 0.0309          | 0.9777    | 0.9766 | 0.9771 | 0.9935   |
| 0.0051        | 12.0  | 1680 | 0.0232          | 0.9820    | 0.9820 | 0.9820 | 0.9951   |
| 0.0051        | 13.0  | 1820 | 0.0344          | 0.9849    | 0.9784 | 0.9816 | 0.9945   |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "disfluency-large-2", "results": []}]}
transformers pytorch tensorboard roberta token-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

disfluency-large-2
==================


This model is a fine-tuned version of vinai/phobert-large on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0318
* Precision: 0.9837
* Recall: 0.9808
* F1: 0.9822
* Accuracy: 0.9946


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
* num\_epochs: 50


### Training results



### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### 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
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### 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
* num\_epochs: 50 ### Training results ### Framework versions


* Transformers 4.29.2
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3