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# punctuation-tedtalk2012-bert-base-full-text-bert

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the tiagoblima/punctuation-tedtalk2012-full-text-bert dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1495
- Precision: 0.7179
- Recall: 0.7416
- F1: 0.7295
- Accuracy: 0.9417

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 197  | 0.1495          | 0.7179    | 0.7416 | 0.7295 | 0.9417   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tiagoblima/punctuation-tedtalk2012-full-text-bert"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "punctuation-tedtalk2012-bert-base-full-text-bert", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "tiagoblima/punctuation-tedtalk2012-full-text-bert", "type": "tiagoblima/punctuation-tedtalk2012-full-text-bert"}, "metrics": [{"type": "precision", "value": 0.7178729689807977, "name": "Precision"}, {"type": "recall", "value": 0.7416073245167853, "name": "Recall"}, {"type": "f1", "value": 0.7295471603702777, "name": "F1"}, {"type": "accuracy", "value": 0.9416717510677243, "name": "Accuracy"}]}]}]}
transformers pytorch bert token-classification generated_from_trainer dataset:tiagoblima/punctuation-tedtalk2012-full-text-bert license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-tiagoblima/punctuation-tedtalk2012-full-text-bert #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 

punctuation-tedtalk2012-bert-base-full-text-bert
================================================


This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the tiagoblima/punctuation-tedtalk2012-full-text-bert dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1495
* Precision: 0.7179
* Recall: 0.7416
* F1: 0.7295
* Accuracy: 0.9417


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: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-tiagoblima/punctuation-tedtalk2012-full-text-bert #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #token-classification #generated_from_trainer #dataset-tiagoblima/punctuation-tedtalk2012-full-text-bert #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 5e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 1.0### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2