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# bloom-NER-fr

This model is a fine-tuned version of [roberta-large-mnli](https://huggingface.co/roberta-large-mnli) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2930
- Precision: 0.5423
- Recall: 0.6361
- F1: 0.5854
- Accuracy: 0.9004

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.7569        | 1.0   | 47   | 0.4836          | 0.3709    | 0.3924 | 0.3813 | 0.8604   |
| 0.4348        | 2.0   | 94   | 0.3771          | 0.4395    | 0.5443 | 0.4863 | 0.8687   |
| 0.3607        | 3.0   | 141  | 0.3232          | 0.5115    | 0.6086 | 0.5559 | 0.8953   |
| 0.2913        | 4.0   | 188  | 0.2918          | 0.5527    | 0.6255 | 0.5868 | 0.8974   |
| 0.2602        | 5.0   | 235  | 0.2835          | 0.5485    | 0.6445 | 0.5926 | 0.9028   |
| 0.2332        | 6.0   | 282  | 0.2930          | 0.5423    | 0.6361 | 0.5854 | 0.9004   |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3

{"language": ["mn"], "license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "bloom-NER-fr", "results": []}]}
transformers pytorch tensorboard roberta token-classification generated_from_trainer mn license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #mn #license-mit #autotrain_compatible #endpoints_compatible #region-us 

bloom-NER-fr
============


This model is a fine-tuned version of roberta-large-mnli on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2930
* Precision: 0.5423
* Recall: 0.6361
* F1: 0.5854
* Accuracy: 0.9004


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: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #roberta #token-classification #generated_from_trainer #mn #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: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 6 ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3