datasets:
- natural_instructions
- the_pile
- cot
- Muennighoff/P3
inference:
parameters:
max_new_tokens: 5
temperature: 1
top_k: 1
license: apache-2.0
language:
- en
pipeline_tag: text-generation
widget:
- example_title: Sentiment Analysis
text: >-
The task is to label the post's emotion as sadness, joy, love, anger,
fear, or surprise.
Input: I'm feeling quite sad and sorry for myself but ill snap out of it
soon.
Output: sadness
Input: I am just feeling cranky and blue.
Output: anger
Input: I can have for a treat or if i am feeling festive.
Output:
- example_title: Country Currency
text: |-
Return the currency of the given country.
Input: Switzerland
Output: Swiss Franc
Input: India
Output:
- example_title: Tweet Eval Hate
text: >-
Label whether the following tweet contains hate speech against either
immigrants or women. Hate Speech (HS) is commonly defined as any
communication that disparages a person or a group on the basis of some
characteristic such as race, color, ethnicity, gender, sexual orientation,
nationality, religion, or other characteristics.
Possible labels:
1. hate speech
2. not hate speech
Tweet: HOW REFRESHING! In South Korea, there is no such thing as
'political correctness" when it comes to dealing with Muslim refugee
wannabes via @user
Label: hate speech
Tweet: New to Twitter-- any men on here know what the process is to get
#verified?
Label: not hate speech
Tweet: Dont worry @user you are and will always be the most hysterical
woman.
Label:
- example_title: Entity Recognition
text: >-
Extract all the names of people, places, and organizations from the
following sentences.
Sentence: Satya Nadella, the CEO of Microsoft, was visiting the Bahamas
last May.
Entities: Satya Nadella, Microsoft, Bahamas
Sentence: Pacific Northwest cities include Seattle and Portland, which I
have visited with Vikash.
Entities:
- example_title: Data Clearning
text: |-
Format the data into a CSV file:
Input: Jane Doe jane.doe@gmail.com (520) 382 2435
Output: Jane Doe,jane.doe@gmail.com,520-382-2435
Input: Peter Lee (510) 333-2429 email: peter@yahoo.com
Output:
GPT-JT
Feel free to try out our Online Demo!
Model Summary
With a new decentralized training algorithm, we fine-tuned GPT-J (6B) on 3.53 billion tokens, resulting in GPT-JT (6B), a model that outperforms many 100B+ parameter models on classification benchmarks.
We incorporated a collection of open techniques and datasets to build GPT-JT:
- GPT-JT is a fork of EleutherAI's GPT-J (6B);
- We used UL2's training objective, allowing the model to see bidirectional context of the prompt;
- The model was trained on a large collection of diverse data, including Chain-of-Thought (CoT), Public Pool of Prompts (P3) dataset, Natural-Instructions (NI) dataset.
With the help of techniques mentioned above, GPT-JT significantly improves the performance of classification tasks over the original GPT-J, and even outperforms most 100B+ parameter models!
Quick Start
from transformers import pipeline
pipe = pipeline(model='togethercomputer/GPT-JT-6B-v1')
pipe('''"I love this!" Is it positive? A:''')
or
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("togethercomputer/GPT-JT-6B-v1")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/GPT-JT-6B-v1")
License
The weights of GPT-JT-6B-v1 are licensed under version 2.0 of the Apache License.
Training Details
UL2 Training Objective
We train GPT-JT using UL2 training objective [1][2]. The original GPT-J uses causal mask (as shown below left) for autoregressive generation. So for each token, it can only see its previous context. In order to fully leverage the context information, we continue to train GPT-J with UL2 training objectives, and uses causal mask with prefix (as shown below right) -- using bidirectional attention for the prompt / input and causal attention for token generation. Intuitively, being able to see context bidirectionally might improve downstream tasks that require this information.
Furthermore, we leverage a large collection of data, including Natural-Instructions, P3, MMLU-COT, and the Pile Specifically, we first conduct training for 2.62 billion tokens using the UL2 loss on the Pile, followed by 0.92 billion tokens with a mixture of the above datasets: 5% of COT, 20% of P3, 20% of NI, and 55% of the Pile.
Hyperparameters
We used AdamW with a learning rate of 1e-5 and global batch size of 64 (16 for each data parallel worker). We used mix-precision training where the activation is in FP16 while the optimizer states are kept in FP32. We use both data parallelism and pipeline parallelism to conduct training. During training, we truncate the input sequence to 2048 tokens, and for input sequence that contains less than 2048 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.
Infrastructure
We used the Together Research Computer to conduct training.
References
[1]: Tay, Yi, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, and Donald Metzler. "Unifying Language Learning Paradigms." arXiv preprint arXiv:2205.05131 (2022).
[2]: Tay, Yi, Jason Wei, Hyung Won Chung, Vinh Q. Tran, David R. So, Siamak Shakeri, Xavier Garcia et al. "Transcending scaling laws with 0.1% extra compute." arXiv preprint arXiv:2210.11399 (2022).