|
--- |
|
language: |
|
- es |
|
tags: |
|
- spanish |
|
- sentiment |
|
datasets: |
|
- muchocine |
|
widget: |
|
- "Increíble pelicula. ¡Altamente recomendado!" |
|
- "Extremadamente malo. Baja calidad" |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# electricidad-base-muchocine-finetuned |
|
|
|
This model fine-tunes [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on [muchocine](https://huggingface.co/datasets/muchocine) dataset for sentiment classification to predict *star_rating*. |
|
|
|
|
|
### How to use |
|
The model can be used directly with the HuggingFace `pipeline`. |
|
```python |
|
from transformers import AutoTokenizer, AutoModelWithLMHead |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("shahp7575/gpt2-horoscopes") |
|
model = AutoModelWithLMHead.from_pretrained("shahp7575/gpt2-horoscopes") |
|
``` |
|
|
|
### Examples |
|
|
|
```python |
|
from transformers import pipeline |
|
clf = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) |
|
|
|
clf('Esta película es una joya. Todo fue perfecto: historia, casting, dirección. Me encantó el clímax.') |
|
>>> [{'label': '5', 'score': 0.9658033847808838}] |
|
|
|
clf("La historia y el casting fueron geniales.") |
|
>>> [{'label': '4', 'score': 0.6666394472122192}] |
|
|
|
clf("Me gustó pero podría ser mejor.") |
|
>>> [{'label': '3', 'score': 0.7013391852378845}] |
|
|
|
clf("dinero tirado en esta pelicula") |
|
>>> [{'label': '2', 'score': 0.7564149498939514}] |
|
|
|
clf("esta película es una película absolutamente repugnante. odio todo al respecto. gastó tanto dinero.") |
|
>>> [{'label': '1', 'score': 0.3040296733379364}] |
|
|
|
``` |
|
|