Update README.md

README.md

@@ -9,28 +9,11 @@ datasets:
 INT8 ONNX version of [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) to use with [Transformers.js](https://huggingface.co/docs/transformers.js).
 
 ### Example usage
-#### Pipeline API
+
 ```js
 import { pipeline } from '@xenova/transformers';
 
-const generator = await pipeline('text-generation', 'Felladrin/onnx-flan-t5-base-samsum');
+const generator = await pipeline('text2text-generation', 'Felladrin/onnx-flan-t5-base-samsum');
 const output =  await generator("Val: it's raining! Candy: I know, just started... Val: r we going? we will be wet Candy: maybe wait a little? see if stops Val: ok. let's wait half h and than see Candy: god idea, I call u then Val: great :)", { add_special_tokens: true, max_new_tokens: 60, repetition_penalty: 1.2});
-console.log(output);
-// It's raining. Val and Candy will wait half an hour and then see if...
-```
-
-#### Auto Classes
-```js
-import { AutoModelForCausalLM, AutoTokenizer } from '@xenova/transformers';
-
-const model_path = 'Felladrin/onnx-flan-t5-base-samsum';
-const model = await AutoModelForCausalLM.from_pretrained(model_path);
-const tokenizer = await AutoTokenizer.from_pretrained(model_path);
-
-const prompt = "Val: it's raining! Candy: I know, just started... Val: r we going? we will be wet Candy: maybe wait a little? see if stops Val: ok. let's wait half h and than see Candy: god idea, I call u then Val: great :)";
-const { input_ids } = tokenizer(prompt);
-const tokens = await model.generate(input_ids, { max_new_tokens: 60, repetition_penalty: 1.2});
-console.log(tokenizer.decode(tokens[0], { skip_special_tokens: true }));
-// It's raining. Val and Candy will wait half an hour and then see if...
-```
-
+console.log(output); // It's raining. Val and Candy will wait half an hour and then see if...
+```