# 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).

{"language": ["es"], "license": "apache-2.0", "tags": ["es", "ticket classification"], "datasets": ["self made to classify whether text is related to technology or not."], "metrics": ["fscore", "accuracy", "precision", "recall"]}
transformers pytorch bert text-classification es ticket classification license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

# 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*:

## Training procedure
I trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased.

# 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*: ## Training procedure
I trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased.
TAGS
#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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*: ## Training procedure
I trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased.
passage: TAGS
#transformers #pytorch #bert #text-classification #es #ticket classification #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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*:## Training procedure
I trained on the dataset on the dccuchile/bert-base-spanish-wwm-cased.