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question-answering mask_token: [MASK]
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							$
							curl -X POST \
-H "Authorization: Bearer YOUR_ORG_OR_USER_API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"question": "Where does she live?", "context": "She lives in Berlin."}' \
https://api-inference.huggingface.co/models/mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es
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mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es
1,259 downloads
last 30 days

pytorch

tf

Contributed by

mrm8488 Manuel Romero
146 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es") model = AutoModelForQuestionAnswering.from_pretrained("mrm8488/bert-base-spanish-wwm-cased-finetuned-spa-squad2-es")

BETO (Spanish BERT) + Spanish SQuAD2.0

This model is provided by BETO team and fine-tuned on SQuAD-es-v2.0 for Q&A downstream task.

Details of the language model('dccuchile/bert-base-spanish-wwm-cased')

Language model ('dccuchile/bert-base-spanish-wwm-cased'):

BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with Multilingual BERT as well as other (not BERT-based) models.

Details of the downstream task (Q&A) - Dataset

SQuAD-es-v2.0

Dataset # Q&A
SQuAD2.0 Train 130 K
SQuAD2.0-es-v2.0 111 K
SQuAD2.0 Dev 12 K
SQuAD-es-v2.0-small Dev 69 K

Model training

The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:

export SQUAD_DIR=path/to/nl_squad
python transformers/examples/question-answering/run_squad.py \
  --model_type bert \
  --model_name_or_path dccuchile/bert-base-spanish-wwm-cased \
  --do_train \
  --do_eval \
  --do_lower_case \
  --train_file $SQUAD_DIR/train_nl-v2.0.json \
  --predict_file $SQUAD_DIR/dev_nl-v2.0.json \
  --per_gpu_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2.0 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /content/model_output \
  --save_steps 5000 \
  --threads 4 \
  --version_2_with_negative 

Results:

Metric # Value
Exact 76.5050
F1 86.0781
{
  "exact": 76.50501430594491,
  "f1": 86.07818773108252,
  "total": 69202,
  "HasAns_exact": 67.93020719738277,
  "HasAns_f1": 82.37912207996466,
  "HasAns_total": 45850,
  "NoAns_exact": 93.34104145255225,
  "NoAns_f1": 93.34104145255225,
  "NoAns_total": 23352,
  "best_exact": 76.51223953064941,
  "best_exact_thresh": 0.0,
  "best_f1": 86.08541295578848,
  "best_f1_thresh": 0.0
}

Model in action (in a Colab Notebook)

  1. Set the context and ask some questions:

Set context and questions

  1. Run predictions:

Run the model

Created by Manuel Romero/@mrm8488

Made with in Spain