|
--- |
|
language: |
|
- es |
|
tags: |
|
- es |
|
- ticket classification |
|
license: "apache-2.0" |
|
datasets: |
|
- self made to classify whether text is related to technology or not. |
|
metrics: |
|
- fscore |
|
- accuracy |
|
- precision |
|
- recall |
|
--- |
|
# BETO(cased) |
|
This model was built using pytorch. |
|
## Model description |
|
Input for the model: Any spanish text |
|
Output for the model: Sentiment. (0 - Negative, 1 - Positive(i.e. technology relate)) |
|
#### How to use |
|
Here is how to use this model to get the features of a given text in *PyTorch*: |
|
```python |
|
# You can include sample code which will be formatted |
|
from transformers import AutoTokenizer, AutoModelForSequenceClassification |
|
tokenizer = AutoTokenizer.from_pretrained("hiiamsid/BETO_es_binary_classification") |
|
model = AutoModelForSequenceClassification.from_pretrained("hiiamsid/BETO_es_binary_classification") |
|
text = "Replace me by any text you'd like." |
|
encoded_input = tokenizer(text, return_tensors='pt') |
|
output = model(**encoded_input) |
|
``` |
|
## Training procedure |
|
I trained on the dataset on the [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased). |
|
|