
# TExAS-SQuAD-fr

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-fr dataset.
It achieves the following results on the evaluation set:
- Exact match: xx.xx%
- F1-score: xx.xx%


## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.1478        | 0.23  | 1000  | 1.8543          |
| 1.9827        | 0.46  | 2000  | 1.7643          |
| 1.8427        | 0.69  | 3000  | 1.6789          |
| 1.8372        | 0.92  | 4000  | 1.6137          |
| 1.7318        | 1.15  | 5000  | 1.6093          |
| 1.6603        | 1.38  | 6000  | 1.7157          |
| 1.6334        | 1.61  | 7000  | 1.6302          |
| 1.6716        | 1.84  | 8000  | 1.5845          |
| 1.5192        | 2.06  | 9000  | 1.6690          |
| 1.5174        | 2.29  | 10000 | 1.6669          |
| 1.4611        | 2.52  | 11000 | 1.6301          |
| 1.4648        | 2.75  | 12000 | 1.6009          |
| 1.5052        | 2.98  | 13000 | 1.6133          |


### Framework versions

- Transformers 4.12.2
- Pytorch 1.8.1+cu101
- Datasets 1.12.1
- Tokenizers 0.10.3

{"license": "mit", "tags": ["generated_from_trainer"], "widget": [{"text": "Comment obtenir la coagulation?", "context": "La coagulation peut \u00eatre obtenue soit par action d'une enzyme, la pr\u00e9sure, soit par fermentation provoqu\u00e9e par des bact\u00e9ries lactiques (le lactose est alors transform\u00e9 en acide lactique), soit tr\u00e8s fr\u00e9quemment par combinaison des deux m\u00e9thodes pr\u00e9c\u00e9dentes, soit par chauffage associ\u00e9 \u00e0 une acidification directe (vinaigre\u2026). On proc\u00e8de ensuite \u00e0 l'\u00e9gouttage. On obtient alors le caill\u00e9 et le lactos\u00e9rum. Le lactos\u00e9rum peut aussi \u00eatre utilis\u00e9 directement : fromage de lactos\u00e9rum comme le s\u00e9rac, ou par r\u00e9incorporation de ses composants."}], "model-index": [{"name": "xlmr-base-texas-squad-fr", "results": []}]}
transformers pytorch tensorboard xlm-roberta question-answering generated_from_trainer license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #endpoints_compatible #region-us 

TExAS-SQuAD-fr
==============


This model is a fine-tuned version of xlm-roberta-base on the TExAS-SQuAD-fr dataset.
It achieves the following results on the evaluation set:


* Exact match: URL%
* F1-score: URL%


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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3


### Training results



### Framework versions


* Transformers 4.12.2
* Pytorch 1.8.1+cu101
* Datasets 1.12.1
* Tokenizers 0.10.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* train\_batch\_size: 8
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.12.2
* Pytorch 1.8.1+cu101
* Datasets 1.12.1
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3 ### Training results ### Framework versions


* Transformers 4.12.2
* Pytorch 1.8.1+cu101
* Datasets 1.12.1
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #license-mit #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
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3### Training results### Framework versions


* Transformers 4.12.2
* Pytorch 1.8.1+cu101
* Datasets 1.12.1
* Tokenizers 0.10.3