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# distilbert-fr-explorer-mlm

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6345

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 65
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.8566        | 1.0   | 611   | 2.5339          |
| 2.5167        | 2.0   | 1222  | 2.3487          |
| 2.3456        | 3.0   | 1833  | 2.2150          |
| 2.2457        | 4.0   | 2444  | 2.1290          |
| 2.1817        | 5.0   | 3055  | 2.0981          |
| 2.1236        | 6.0   | 3666  | 2.0542          |
| 2.0782        | 7.0   | 4277  | 2.0002          |
| 2.0448        | 8.0   | 4888  | 1.9860          |
| 1.9967        | 9.0   | 5499  | 1.9708          |
| 1.9806        | 10.0  | 6110  | 1.9546          |
| 1.9358        | 11.0  | 6721  | 1.9269          |
| 1.9166        | 12.0  | 7332  | 1.8638          |
| 1.8908        | 13.0  | 7943  | 1.8710          |
| 1.8716        | 14.0  | 8554  | 1.8679          |
| 1.8515        | 15.0  | 9165  | 1.8462          |
| 1.8233        | 16.0  | 9776  | 1.8517          |
| 1.813         | 17.0  | 10387 | 1.8010          |
| 1.7974        | 18.0  | 10998 | 1.8096          |
| 1.7827        | 19.0  | 11609 | 1.8032          |
| 1.7693        | 20.0  | 12220 | 1.7867          |
| 1.7494        | 21.0  | 12831 | 1.7882          |
| 1.7417        | 22.0  | 13442 | 1.7830          |
| 1.7252        | 23.0  | 14053 | 1.7942          |
| 1.7041        | 24.0  | 14664 | 1.7697          |
| 1.6983        | 25.0  | 15275 | 1.7566          |
| 1.6817        | 26.0  | 15886 | 1.7391          |
| 1.6803        | 27.0  | 16497 | 1.7389          |
| 1.6689        | 28.0  | 17108 | 1.7165          |
| 1.6558        | 29.0  | 17719 | 1.7475          |
| 1.6523        | 30.0  | 18330 | 1.7136          |
| 1.6387        | 31.0  | 18941 | 1.7033          |
| 1.6352        | 32.0  | 19552 | 1.7056          |
| 1.6221        | 33.0  | 20163 | 1.6910          |
| 1.6051        | 34.0  | 20774 | 1.6908          |
| 1.6083        | 35.0  | 21385 | 1.6688          |
| 1.6011        | 36.0  | 21996 | 1.6876          |
| 1.5903        | 37.0  | 22607 | 1.6900          |
| 1.5896        | 38.0  | 23218 | 1.6942          |
| 1.5772        | 39.0  | 23829 | 1.6957          |
| 1.5731        | 40.0  | 24440 | 1.6805          |
| 1.5727        | 41.0  | 25051 | 1.6717          |
| 1.566         | 42.0  | 25662 | 1.6848          |
| 1.5588        | 43.0  | 26273 | 1.6788          |
| 1.5549        | 44.0  | 26884 | 1.6919          |
| 1.5444        | 45.0  | 27495 | 1.6610          |
| 1.5372        | 46.0  | 28106 | 1.6602          |
| 1.5426        | 47.0  | 28717 | 1.6669          |
| 1.5262        | 48.0  | 29328 | 1.6666          |
| 1.5287        | 49.0  | 29939 | 1.6461          |
| 1.5241        | 50.0  | 30550 | 1.6612          |
| 1.5257        | 51.0  | 31161 | 1.6447          |
| 1.522         | 52.0  | 31772 | 1.6483          |
| 1.5153        | 53.0  | 32383 | 1.6350          |
| 1.5066        | 54.0  | 32994 | 1.6592          |
| 1.5119        | 55.0  | 33605 | 1.6635          |
| 1.5029        | 56.0  | 34216 | 1.6375          |
| 1.5017        | 57.0  | 34827 | 1.6346          |
| 1.5052        | 58.0  | 35438 | 1.6257          |
| 1.4952        | 59.0  | 36049 | 1.6452          |
| 1.5043        | 60.0  | 36660 | 1.6417          |
| 1.5018        | 61.0  | 37271 | 1.6203          |
| 1.492         | 62.0  | 37882 | 1.6358          |
| 1.4955        | 63.0  | 38493 | 1.6450          |
| 1.5017        | 64.0  | 39104 | 1.6373          |
| 1.4972        | 65.0  | 39715 | 1.6345          |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "distilbert-fr-explorer-mlm", "results": []}]}
transformers pytorch distilbert fill-mask generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

distilbert-fr-explorer-mlm
==========================


This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 1.6345


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: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 65
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 65
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #distilbert #fill-mask #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 32
* eval\_batch\_size: 32
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 65
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.28.1
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.2