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# twhin-bert-base-finetuned-twhin-epoch

This model is a fine-tuned version of [Twitter/twhin-bert-base](https://huggingface.co/Twitter/twhin-bert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8164
- Precision: 0.8381
- Recall: 0.8347
- F1: 0.8360

## 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: 1e-05
- train_batch_size: 36
- eval_batch_size: 36
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log        | 1.0   | 52   | 0.5851          | 0.7441    | 0.7796 | 0.7613 |
| No log        | 2.0   | 104  | 0.5324          | 0.7630    | 0.7861 | 0.7700 |
| No log        | 3.0   | 156  | 0.4893          | 0.7774    | 0.8104 | 0.7923 |
| No log        | 4.0   | 208  | 0.5204          | 0.7862    | 0.8104 | 0.7936 |
| No log        | 5.0   | 260  | 0.5753          | 0.7728    | 0.8120 | 0.7907 |
| No log        | 6.0   | 312  | 0.5552          | 0.7729    | 0.8071 | 0.7889 |
| No log        | 7.0   | 364  | 0.5975          | 0.7768    | 0.8136 | 0.7946 |
| No log        | 8.0   | 416  | 0.6527          | 0.8015    | 0.8055 | 0.7915 |
| No log        | 9.0   | 468  | 0.6521          | 0.8285    | 0.8233 | 0.8252 |
| 0.3755        | 10.0  | 520  | 0.6629          | 0.8315    | 0.8104 | 0.8175 |
| 0.3755        | 11.0  | 572  | 0.7238          | 0.8260    | 0.8266 | 0.8263 |
| 0.3755        | 12.0  | 624  | 0.7782          | 0.8318    | 0.8201 | 0.8239 |
| 0.3755        | 13.0  | 676  | 0.7788          | 0.8263    | 0.8266 | 0.8260 |
| 0.3755        | 14.0  | 728  | 0.8164          | 0.8381    | 0.8347 | 0.8360 |
| 0.3755        | 15.0  | 780  | 0.8701          | 0.8238    | 0.8201 | 0.8212 |
| 0.3755        | 16.0  | 832  | 0.8774          | 0.8295    | 0.8282 | 0.8288 |
| 0.3755        | 17.0  | 884  | 0.9193          | 0.8311    | 0.8233 | 0.8259 |
| 0.3755        | 18.0  | 936  | 0.9321          | 0.8339    | 0.8282 | 0.8299 |
| 0.3755        | 19.0  | 988  | 0.9350          | 0.8307    | 0.8233 | 0.8261 |
| 0.0554        | 20.0  | 1040 | 0.9344          | 0.8256    | 0.8185 | 0.8213 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "model-index": [{"name": "twhin-bert-base-finetuned-twhin-epoch", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

twhin-bert-base-finetuned-twhin-epoch
=====================================


This model is a fine-tuned version of Twitter/twhin-bert-base on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.8164
* Precision: 0.8381
* Recall: 0.8347
* F1: 0.8360


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: 1e-05
* train\_batch\_size: 36
* eval\_batch\_size: 36
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 36
* eval\_batch\_size: 36
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 36
* eval\_batch\_size: 36
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 1e-05
* train\_batch\_size: 36
* eval\_batch\_size: 36
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 20### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu117
* Datasets 2.10.1
* Tokenizers 0.13.2