
# ixambert-base-cased finetuned for QA

This is a basic implementation of the multilingual model ["ixambert-base-cased"](https://huggingface.co/ixa-ehu/ixambert-base-cased), fine-tuned on SQuAD v1.1, that is able to answer basic factual questions in English, Spanish and Basque.

## Overview

* **Language model:** ixambert-base-cased
* **Languages:** English, Spanish and Basque
* **Downstream task:** Extractive QA
* **Training data:** SQuAD v1.1
* **Eval data:** SQuAD v1.1
* **Infrastructure:** 1x GeForce RTX 2080

## Outputs

The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example:

```python
{'score': 0.9667195081710815, 'start': 101, 'end': 105, 'answer': '1820'}
```

## How to use

```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline

model_name = "MarcBrun/ixambert-finetuned-squad"

# To get predictions
context = "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820"
question = "When was Florence Nightingale born?"
qa = pipeline("question-answering", model=model_name, tokenizer=model_name)
pred = qa(question=question,context=context)

# To load the model and tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```

## Hyperparameters

```
batch_size = 8
n_epochs = 3
learning_rate = 2e-5
optimizer = AdamW
lr_schedule = linear
max_seq_len = 384
doc_stride = 128
``` 
{"language": ["en", "es", "eu"], "datasets": ["squad"], "widget": [{"text": "When was Florence Nightingale born?", "context": "Florence Nightingale, known for being the founder of modern nursing, was born in Florence, Italy, in 1820.", "example_title": "English"}, {"text": "\u00bfPor qu\u00e9 provincias pasa el Tajo?", "context": "El Tajo es el r\u00edo m\u00e1s largo de la pen\u00ednsula ib\u00e9rica, a la que atraviesa en su parte central, siguiendo un rumbo este-oeste, con una leve inclinaci\u00f3n hacia el suroeste, que se acent\u00faa cuando llega a Portugal, donde recibe el nombre de Tejo.\nNace en los montes Universales, en la sierra de Albarrac\u00edn, sobre la rama occidental del sistema Ib\u00e9rico y, despu\u00e9s de recorrer 1007 km, llega al oc\u00e9ano Atl\u00e1ntico en la ciudad de Lisboa. En su desembocadura forma el estuario del mar de la Paja, en el que vierte un caudal medio de 456 m\u00b3/s. En sus primeros 816 km atraviesa Espa\u00f1a, donde discurre por cuatro comunidades aut\u00f3nomas (Arag\u00f3n, Castilla-La Mancha, Madrid y Extremadura) y un total de seis provincias (Teruel, Guadalajara, Cuenca, Madrid, Toledo y C\u00e1ceres).", "example_title": "Espa\u00f1ol"}, {"text": "Zer beste izenak ditu Tartalo?", "context": "Tartalo euskal mitologiako izaki begibakar artzain erraldoia da. Tartalo izena zenbait euskal hizkeratan herskari-bustidurarekin ahoskatu ohi denez, horrelaxe ere idazten da batzuetan: Ttarttalo. Euskal Herriko zenbait tokitan, Torto edo Anxo ere esaten diote.", "example_title": "Euskara"}]}
transformers pytorch bert question-answering en es eu dataset:squad endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #question-answering #en #es #eu #dataset-squad #endpoints_compatible #has_space #region-us 


# ixambert-base-cased finetuned for QA

This is a basic implementation of the multilingual model "ixambert-base-cased", fine-tuned on SQuAD v1.1, that is able to answer basic factual questions in English, Spanish and Basque.

## Overview

* Language model: ixambert-base-cased
* Languages: English, Spanish and Basque
* Downstream task: Extractive QA
* Training data: SQuAD v1.1
* Eval data: SQuAD v1.1
* Infrastructure: 1x GeForce RTX 2080

## Outputs

The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example:



## How to use



## Hyperparameters

 
# ixambert-base-cased finetuned for QA

This is a basic implementation of the multilingual model "ixambert-base-cased", fine-tuned on SQuAD v1.1, that is able to answer basic factual questions in English, Spanish and Basque. ## Overview

* Language model: ixambert-base-cased
* Languages: English, Spanish and Basque
* Downstream task: Extractive QA
* Training data: SQuAD v1.1
* Eval data: SQuAD v1.1
* Infrastructure: 1x GeForce RTX 2080 ## Outputs

The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example: ## How to use ## Hyperparameters
TAGS
#transformers #pytorch #bert #question-answering #en #es #eu #dataset-squad #endpoints_compatible #has_space #region-us 
 # ixambert-base-cased finetuned for QA

This is a basic implementation of the multilingual model "ixambert-base-cased", fine-tuned on SQuAD v1.1, that is able to answer basic factual questions in English, Spanish and Basque. ## Overview

* Language model: ixambert-base-cased
* Languages: English, Spanish and Basque
* Downstream task: Extractive QA
* Training data: SQuAD v1.1
* Eval data: SQuAD v1.1
* Infrastructure: 1x GeForce RTX 2080 ## Outputs

The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example: ## How to use ## Hyperparameters
passage: TAGS
#transformers #pytorch #bert #question-answering #en #es #eu #dataset-squad #endpoints_compatible #has_space #region-us 
# ixambert-base-cased finetuned for QA

This is a basic implementation of the multilingual model "ixambert-base-cased", fine-tuned on SQuAD v1.1, that is able to answer basic factual questions in English, Spanish and Basque.## Overview

* Language model: ixambert-base-cased
* Languages: English, Spanish and Basque
* Downstream task: Extractive QA
* Training data: SQuAD v1.1
* Eval data: SQuAD v1.1
* Infrastructure: 1x GeForce RTX 2080## Outputs

The model outputs the answer to the question, the start and end positions of the answer in the original context, and a score for the probability for that span of text to be the correct answer. For example:## How to use## Hyperparameters