Update README.md

README.md

@@ -36,7 +36,7 @@ inference:
 
 `Aira-Instruct-124M` is a instruction-tuned GPT-style model based on [GPT-2](https://huggingface.co/gpt2). The model was trained with a dataset composed of `prompt`, `completions`, generated via the [Self-Instruct](https://github.com/yizhongw/self-instruct) framework. `Aira-Instruct-124M` instruction-tuning was achieved via conditional text generation.
 
-The dataset used to train this model combines two main sources of data: the [`synthetic-instruct-gptj-pairwise`](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) dataset and a subset of [Aira's](https://github.com/Nkluge-correa/Aira-EXPERT) fine-tuning dataset focused on Ethics, AI, AI safety, and related topics. The dataset is available in both Portuguese and English.
+The dataset used to train this model combines the following sources of data: the [`synthetic-instruct-gptj-pairwise`](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) dataset, the [`databricks_dolly_15k`](https://huggingface.co/datasets/HuggingFaceH4/databricks_dolly_15k) dataset, the [`instruction-dataset`](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset) dataset, and a subset of [Aira's](https://github.com/Nkluge-correa/Aira-EXPERT) fine-tuning dataset, focused on Q&A related to Ethics, AI, AI safety, and other related topics. The dataset is available in both Portuguese and English.
 
 Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo).
 
@@ -64,7 +64,7 @@ This repository has the notebook used to train this model.
 
 ## Usage
 
-Two special tokens are used to mark the user side of the interaction and the model's response: 
+Two special tokens are used to mark the user side of the interaction and the model's response:
 
 `<|startoftext|>`What is a language model?`<|endoftext|>`A language model is a probability distribution over a vocabulary.`<|endoftext|>`
 
@@ -98,7 +98,6 @@ responses = aira.generate(**inputs,
 print(f"Question: 👤 {question}\n")
 
 for i, response in  enumerate(responses):
-	# print only the response and remove the question
 	print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, "")}')
 ```
 
@@ -110,6 +109,7 @@ The model will output something like:
 >>>Response 1: 🤖 Hi there! I am Aira, a chatbot designed to answer questions about AI ethics and AI safety. If you need assistance navigating our conversation, please feel free to ask!
 >>>Response 2: 🤖 Hi there! My name is Aira, and I'm a chatbot designed to answer questions related to AI ethics and AI Safety. If you need assistance, feel free to ask, and I'll be happy to help you out.
 ```
+
 ## Limitations
 
 🤥 Generative models can perpetuate the generation of pseudo-informative content, that is, false information that may appear truthful. For example, multi-modal generative models can be used to create images with untruthful content, while language models for text generation can automate the generation of misinformation.
@@ -134,4 +134,4 @@ The model will output something like:
 
 ## License
 
-The `Aira-Instruct-124M` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.
+The `Aira-Instruct-124M` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.