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# camembert-keyword-discriminator

This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2180
- Precision: 0.6646
- Recall: 0.7047
- Accuracy: 0.9344
- F1: 0.6841
- Ent/precision: 0.7185
- Ent/accuracy: 0.8157
- Ent/f1: 0.7640
- Con/precision: 0.5324
- Con/accuracy: 0.4860
- Con/f1: 0.5082

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | Accuracy | F1     | Ent/precision | Ent/accuracy | Ent/f1 | Con/precision | Con/accuracy | Con/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:|:-------------:|:------------:|:------:|:-------------:|:------------:|:------:|
| 0.2016        | 1.0   | 1875  | 0.1910          | 0.5947    | 0.7125 | 0.9243   | 0.6483 | 0.6372        | 0.8809       | 0.7395 | 0.4560        | 0.3806       | 0.4149 |
| 0.1454        | 2.0   | 3750  | 0.1632          | 0.6381    | 0.7056 | 0.9324   | 0.6701 | 0.6887        | 0.8291       | 0.7524 | 0.5064        | 0.4621       | 0.4833 |
| 0.1211        | 3.0   | 5625  | 0.1702          | 0.6703    | 0.6678 | 0.9343   | 0.6690 | 0.7120        | 0.7988       | 0.7529 | 0.5471        | 0.4094       | 0.4684 |
| 0.1021        | 4.0   | 7500  | 0.1745          | 0.6777    | 0.6708 | 0.9351   | 0.6742 | 0.7206        | 0.7956       | 0.7562 | 0.5557        | 0.4248       | 0.4815 |
| 0.0886        | 5.0   | 9375  | 0.1913          | 0.6540    | 0.7184 | 0.9340   | 0.6847 | 0.7022        | 0.8396       | 0.7648 | 0.5288        | 0.4795       | 0.5030 |
| 0.0781        | 6.0   | 11250 | 0.2021          | 0.6605    | 0.7132 | 0.9344   | 0.6858 | 0.7139        | 0.8258       | 0.7658 | 0.5293        | 0.4913       | 0.5096 |
| 0.0686        | 7.0   | 13125 | 0.2127          | 0.6539    | 0.7132 | 0.9337   | 0.6822 | 0.7170        | 0.8172       | 0.7638 | 0.5112        | 0.5083       | 0.5098 |
| 0.0667        | 8.0   | 15000 | 0.2180          | 0.6646    | 0.7047 | 0.9344   | 0.6841 | 0.7185        | 0.8157       | 0.7640 | 0.5324        | 0.4860       | 0.5082 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "model-index": [{"name": "camembert-keyword-discriminator", "results": []}]}
transformers pytorch camembert token-classification generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 

camembert-keyword-discriminator
===============================


This model is a fine-tuned version of camembert-base on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2180
* Precision: 0.6646
* Recall: 0.7047
* Accuracy: 0.9344
* F1: 0.6841
* Ent/precision: 0.7185
* Ent/accuracy: 0.8157
* Ent/f1: 0.7640
* Con/precision: 0.5324
* Con/accuracy: 0.4860
* Con/f1: 0.5082


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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #camembert #token-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 8
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1