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# TwitchLeagueBert-1000k-finetuned-highlight-detection

This model is a fine-tuned version of [Epidot/TwitchLeagueBert-1000k](https://huggingface.co/Epidot/TwitchLeagueBert-1000k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1146
- Precision: 0.4420
- F1: 0.3977
- Recall: 0.3614

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | F1     | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|
| 0.0205        | 1.04  | 20000 | 0.1119          | 0.4665    | 0.4689 | 0.4712 |
| 0.0091        | 2.09  | 40000 | 0.1086          | 0.4803    | 0.4447 | 0.4139 |
| 0.0062        | 3.13  | 60000 | 0.1172          | 0.4382    | 0.4192 | 0.4018 |
| 0.0038        | 4.18  | 80000 | 0.1146          | 0.4420    | 0.3977 | 0.3614 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.7.1
- Datasets 2.0.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "metrics": ["precision", "f1", "recall"], "model-index": [{"name": "TwitchLeagueBert-1000k-finetuned-highlight-detection", "results": []}]}
transformers pytorch roberta text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 

TwitchLeagueBert-1000k-finetuned-highlight-detection
====================================================


This model is a fine-tuned version of Epidot/TwitchLeagueBert-1000k on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1146
* Precision: 0.4420
* F1: 0.3977
* Recall: 0.3614


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: 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: 10


### Training results



### Framework versions


* Transformers 4.18.0
* Pytorch 1.7.1
* Datasets 2.0.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.7.1
* Datasets 2.0.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10 ### Training results ### Framework versions


* Transformers 4.18.0
* Pytorch 1.7.1
* Datasets 2.0.0
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #roberta #text-classification #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 10### Training results### Framework versions


* Transformers 4.18.0
* Pytorch 1.7.1
* Datasets 2.0.0
* Tokenizers 0.12.1