Update README.md

README.md

@@ -1,3 +1,70 @@
----
-license: apache-2.0
----
+---
+library_name: transformers
+tags:
+- seq2seq
+license: apache-2.0
+datasets:
+- Helsinki-NLP/europarl
+- Helsinki-NLP/opus-100
+language:
+- en
+- it
+base_model:
+- google/t5-efficient-tiny
+pipeline_tag: translation
+---
+
+## 🍃 Foglietta - A super tiny model for English -> Italian translation
+
+Foglietta is an encoder-decoder transformer model for English-Italian text translation based on `google/t5-efficient-tiny`. It was trained on the `en-it` section of `Helsinki-NLP/opus-100` and `Helsinki-NLP/europarl`.
+
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load model and tokenizer from checkpoint directory
+tokenizer = AutoTokenizer.from_pretrained("LeonardPuettmann/Foglietta-mt-en-it")
+model = AutoModelForSeq2SeqLM.from_pretrained("LeonardPuettmann/Foglietta-mt-en-it")
+
+def generate_response(input_text):
+    input_ids = tokenizer("translate English to Italian:" + input_text, return_tensors="pt").input_ids
+    output = model.generate(input_ids, max_new_tokens=256)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+text_to_translate = "I would like a cup of green tea, please."
+response = generate_response(text_to_translate)
+print(response)
+```
+
+As this model is trained on translating sentence pairs, it is best to split longer text into individual sentences, ideally using SpaCy. You can then translate the sentences and join the translations at the end like this:
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import spacy
+# First, install spaCy and the English language model if you haven't already
+# !pip install spacy
+# !python -m spacy download en_core_web_sm
+
+nlp = spacy.load("en_core_web_sm")
+
+tokenizer = AutoTokenizer.from_pretrained("LeonardPuettmann/Foglietta-mt-en-it")
+model = AutoModelForSeq2SeqLM.from_pretrained("LeonardPuettmann/Foglietta-mt-en-it")
+
+def generate_response(input_text):
+    input_ids = tokenizer("translate Italian to English: " + input_text, return_tensors="pt").input_ids
+    output = model.generate(input_ids, max_new_tokens=256)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+text = "How are you doing? Today is a beautiful day. I hope you are doing fine."
+doc = nlp(text)
+sentences = [sent.text for sent in doc.sents]
+
+sentence_translations = []
+for i, sentence in enumerate(sentences):
+    sentence_translation = generate_response(sentence)
+    sentence_translations.append(sentence_translation)
+
+full_translation = " ".join(sentence_translations)
+print(full_translation)
+```