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README.md

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+# Cheapity3 🐷
+GPT3-like T5 model trained to generate text in multiple languages.
+
+## Motivation
+- GPT models are expensive run.
+- GPT models are monolingual.
+
+## Solution
+- Maybe, Small Models aren't Terrible (*SMarT*) 
+- Plus, they are cheaper to run.
+
+I fine-tuned T5 on multiple languages (🇬🇧 English, 🇩🇪 German, 🇫🇷 French) and multiple academic text snippets from various 
+domains like tech, law, finance and science etc. to generate text, just like GPT models do.
+
+## Usage
+- Provide some text e.g `"Italy, officially the Italian Republic is a country consisting of"`
+- Tell Cheapity3 how many words you want to generate e.g `15` -- 😃 Yes, you can control the length.
+- Cheapity3 reads your text and generates a continuation containing approximately 15 words.
+
+```python
+from transformers import AutoTokenizer, AutoModelWithLMHead
+
+tokenizer = AutoTokenizer.from_pretrained("flexudy/cheapity3")
+
+model = AutoModelWithLMHead.from_pretrained("flexudy/cheapity3")
+
+input_text = "guess: Italy, officially the Italian Republic is a country consisting of { _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ }" # 15 words
+
+inputs = tokenizer.encode(input_text, return_tensors="pt", truncation=True, max_length=512)
+
+input_ids = inputs["input_ids"]
+
+attention_mask = inputs["attention_mask"]
+
+outputs = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            max_length=128,
+            do_sample=True,
+            early_stopping=True,
+            num_return_sequences=4,
+            repetition_penalty=2.5
+        )
+
+for i in range(4):
+    print(tokenizer.decode(outputs[i], skip_special_tokens=True, clean_up_tokenization_spaces=True))
+
+# > 
+# > 
+# > 
+# > 
+```
+
+#