Create README.md

README.md

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+---
+language:
+- es
+metrics:
+- bleu
+base_model:
+- vgaraujov/bart-base-spanish
+pipeline_tag: text2text-generation
+library_name: transformers
+tags:
+- gec
+- spanish
+- seq2seq
+- bart
+- cows-l2h
+---
+
+This model has been trained on 80% of the COWS-L2H dataset and 80,984 SYNTHETICALLY-GENERATED errorful sentences for grammatical error correction of Spanish text. The corpus was sentencized, so the model has been fine-tuned for SENTENCE CORRECTION. This model will likely not perform well on an entire paragraph. To correct a paragraph, sentencize the text and run the model for each sentence.
+
+The synthetic data was generated based on a rule-based algorithm from well-formed Spanish sentences. The code for synthetic generaton is available in the Github repo for this project: https://github.com/SkitCon/synth_gec_es
+BLEU: 0.794 on COWS-L2H
+
+Example usage:
+
+```python
+from transformers import AutoTokenizer, BartForConditionalGeneration
+
+tokenizer = AutoTokenizer.from_pretrained("SkitCon/gec-spanish-BARTO-SYNTHETIC")
+model = BartForConditionalGeneration.from_pretrained("SkitCon/gec-spanish-BARTO-SYNTHETIC")
+
+input_sentences = ["Yo va al tienda.", "Espero que tú ganas."]
+
+tokenized_text = tokenizer(input_sentences, return_tensors="pt")
+
+input_ids = source_enc["input_ids"].squeeze()
+attention_mask = source_enc["attention_mask"].squeeze()
+
+outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask)
+
+for sentence in tokenizer.batch_decode(outputs, skip_special_tokens=True):
+  print(sentence)
+```