Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text2text-generation
+---
+
+**flan-t5-small-for-classification**
+
+<img src="https://github.com/Knowledgator/unlimited_classifier/raw/main/images/tree.jpeg" style="display: block; margin: auto;" height="720" width="720">
+
+This is an additional fine-tuned [flan-t5-small](https://huggingface.co/google/flan-t5-small) model on many classification datasets. 
+
+The model supports prompt-tuned classification and is suitable for complex classification settings such as resumes classification by criteria.
+
+You can use the model simply generating the text class name or using our [unlimited-classifier](https://github.com/Knowledgator/unlimited_classifier).
+
+The library allows to set constraints on generation and classify text into millions of classes.
+
+### How to use:
+
+To use it with transformers library take a look into the following code snippet:
+```python
+# pip install accelerate
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
+model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto")
+
+input_text = "Define sentiment of the following text: I love to travel and someday I will see the world."
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
+
+outputs = model.generate(input_ids)
+print(tokenizer.decode(outputs[0]))
+```
+
+**Using unlimited-classifier**
+
+```python
+# pip install unlimited-classifier
+
+from unlimited_classifier import TextClassifier
+
+classifier = TextClassifier(
+    labels=[
+        'positive',
+        'negative',
+        'neutral'    
+    ],
+    model='knowledgator/flan-t5-small-for-classification',
+    tokenizer='knowledgator/flan-t5-small-for-classification',
+)
+output = classifier.invoke(input_text)
+print(output)
+```