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