
# TExAS-SQuAD-es

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the TExAS-SQuAD-es 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.0645        | 0.24  | 1000  | 1.7915          |
| 1.8458        | 0.47  | 2000  | 1.7873          |
| 1.8208        | 0.71  | 3000  | 1.6628          |
| 1.7743        | 0.95  | 4000  | 1.5684          |
| 1.5636        | 1.18  | 5000  | 1.5686          |
| 1.6017        | 1.42  | 6000  | 1.5484          |
| 1.6271        | 1.66  | 7000  | 1.5173          |
| 1.5975        | 1.89  | 8000  | 1.5209          |
| 1.477         | 2.13  | 9000  | 1.5766          |
| 1.4389        | 2.37  | 10000 | 1.5392          |
| 1.3389        | 2.6   | 11000 | 1.5298          |
| 1.437         | 2.84  | 12000 | 1.5504          |


### 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": "\u00bfQui\u00e9n invit\u00f3 a Ra\u00edsa Gorbachova a tomar una copa?", "context": "Las tapas han llegado a convertirse en una se\u00f1al de identidad espa\u00f1ola y son ofrecidas en los banquetes de recepci\u00f3n a los m\u00e1s altos dignatarios (en los denominados tapas meeting). As\u00ed, durante la Conferencia de Paz de Madrid la Reina Sof\u00eda y el alcalde de Madrid Jos\u00e9 Mar\u00eda \u00c1lvarez del Manzano invitaron a Ra\u00edsa Gorbachova a una bebida con tapa durante su visita a la capital espa\u00f1ola. En la modernidad existen bares que ofrecen especialidades de tapas y a este fen\u00f3meno se le ha denominado cocina en miniatura. No obstante, el concepto de tapa ha sido llevado a la alta cocina por el cocinero Ferran Adri\u00e0 que los emplea como entradas."}], "base_model": "xlm-roberta-base", "model-index": [{"name": "xlmr-base-texas-squad-es", "results": []}]}
transformers pytorch tensorboard xlm-roberta question-answering generated_from_trainer base_model:xlm-roberta-base license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #xlm-roberta #question-answering #generated_from_trainer #base_model-xlm-roberta-base #license-mit #endpoints_compatible #region-us 

TExAS-SQuAD-es
==============


This model is a fine-tuned version of xlm-roberta-base on the TExAS-SQuAD-es 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 #base_model-xlm-roberta-base #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 #base_model-xlm-roberta-base #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