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- I added an example using the model with pipelines to show that we have set```{"use_fast": False}``` in the tokenizer.
- I added a Colab to play with the model and pipelines
- I added a Colab to discover Huggingface pipelines at the end of the document
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Manuel Romero
2020-02-20 18:30:54 +01:00
committed by Julien Chaumond
parent cc6775cdf5
commit 7df12d7bf8
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model_cards/mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es/README.md
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@@ -89,7 +89,37 @@ The model was trained on a Tesla P100 GPU and 25GB of RAM with the following com
So, yes, this version is even more accurate.
### Model in action (in a Colab Notebook)
### Model in action
Fast usage with **pipelines**:
```python
from transformers import *
# Important!: By now the QA pipeline is not compatible with fast tokenizer, but they are working on it. So that pass the object to the tokenizer {"use_fast": False} as in the following example:
nlp = pipeline(
'question-answering',
model='mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',
tokenizer=(
'mrm8488/distill-bert-base-spanish-wwm-cased-finetuned-spa-squad2-es',
{"use_fast": False}
)
)
nlp(
{
'question': '¿Para qué lenguaje está trabajando?',
'context': 'Manuel Romero está colaborando activamente con huggingface/transformers ' +
'para traer el poder de las últimas técnicas de procesamiento de lenguaje natural al idioma español'
}
)
# Output: {'answer': 'español', 'end': 169, 'score': 0.67530957344621, 'start': 163}
```
Play with this model and ```pipelines``` in a Colab:
<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Using_Spanish_BERT_fine_tuned_for_Q%26A_pipelines.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
<details>
@@ -100,13 +130,12 @@ So, yes, this version is even more accurate.
2. Run predictions:
![Run the model](https://media.giphy.com/media/WT453aptcbCP7hxWTZ/giphy.gif)
3. Using **Pipelines**
![qa-pipelines](https://media.giphy.com/media/L4YiWhinC3P1GF31t0/giphy.gif)
</details>
More about ``` Huggingface pipelines```? check this Colab out:
<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Huggingface_pipelines_demo.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">&hearts;</span> in Spain