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# camembert-finetuned-ner

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: 0.2215
- Precision: 0.8871
- Recall: 0.9019
- F1: 0.8944
- Accuracy: 0.9476

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2902        | 1.0   | 2500 | 0.2712          | 0.8608    | 0.8755 | 0.8681 | 0.9364   |
| 0.2127        | 2.0   | 5000 | 0.2221          | 0.8858    | 0.8934 | 0.8896 | 0.9437   |
| 0.1575        | 3.0   | 7500 | 0.2215          | 0.8871    | 0.9019 | 0.8944 | 0.9476   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1

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

camembert-finetuned-ner
=======================


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


* Loss: 0.2215
* Precision: 0.8871
* Recall: 0.9019
* F1: 0.8944
* Accuracy: 0.9476


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: 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: 3


### Training results



### Framework versions


* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 3 ### Training results ### Framework versions


* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1
TAGS
#transformers #pytorch #tensorboard #safetensors #camembert #token-classification #generated_from_trainer #base_model-camembert-base #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-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: 3 ### Training results ### Framework versions


* Transformers 4.35.2
* Pytorch 2.1.0+cu121
* Datasets 2.16.1
* Tokenizers 0.15.1