Update README.md

README.md

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-Here, i have finetuned BETO cased spanish bert model for binary classification on spanish language.
-
-Dataset:
-- Text Technology classification
-
-Metrics:
-- loss
+---
+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
-
-language: 
-- es
+- 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).