license: apache-2.0
language:
- en
model-index:
- name: Graphcore/gptj-mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
split: validation_mismatched
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.825
config: mnli_mismatched
datasets:
- glue
tags:
- pytorch
- causal-lm
- text-classification
- text-generation
pipeline_task:
- text-generation
widget:
- text: >-
mnli hypothesis: Your contributions were of no help with our students'
education. premise: Your contribution helped make it possible for us to
provide our students with a quality education. target:
Graphcore/gptj-mnli
This model is the fine-tuned version of EleutherAI/gpt-j-6B on the GLUE MNLI dataset.
MNLI dataset consists of pairs of sentences, a premise and a hypothesis. The task is to predict the relation between the premise and the hypothesis, which can be:
entailment: hypothesis follows from the premise,contradiction: hypothesis contradicts the premise,neutral: hypothesis and premise are unrelated.
We finetune the model as a Causal Language Model (CLM): given a sequence of tokens, the task is to predict the next token. To achieve this, we create a stylised prompt string, following the approach of T5 paper.
mnli hypothesis: {hypothesis} premise: {premise} target: {class_label} <|endoftext|>
For example:
mnli hypothesis: Your contributions were of no help with our students' education. premise: Your contribution helped make it possible for us to provide our students with a quality education. target: contradiction <|endoftext|>
Fine-tuning and validation data
Fine tuning is done using the train split of the GLUE MNLI dataset and the performance is measured using the validation_mismatched split.
validation_mismatched means validation examples are not derived from the same sources as those in the training set and therefore not closely resembling any of the examples seen at training time.
Data splits for the mnli dataset are the following
| train | validation_matched | validation_mismatched |
|---|---|---|
| 392702 | 9815 | 9832 |
Fine-tuning procedure
Fine tuned on a Graphcore IPU-POD64 using popxl.
Prompt sentences are tokenized and packed together to form 1024 token sequences, following HF packing algorithm. No padding is used. The packing process works in groups of 1000 examples and discards any remainder from each group that isn't a whole sequence. For the 392,702 training examples this gives a total of 17,762 sequences per epoch.
Since the model is trained to predict the next token, labels are simply the input sequence shifted by one token. Given the training format, no extra care is needed to account for different sequences: the model does not need to know which sentence a token belongs to.
Hyperparameters:
- optimiser: AdamW (beta1: 0.9, beta2: 0.999, eps: 1e-6, weight decay: 0.0, learning rate: 5e-6)
- learning rate schedule: warmup schedule (min: 1e-7, max: 5e-6, warmup proportion: 0.005995)
- batch size: 128
- training steps: 300. Each epoch consists of ceil(17,762/128) steps, hence 300 steps are approximately 2 epochs.
Performance
The resulting model matches SOTA performance with 82.5% accuracy.
Total number of examples 9832
Number with badly formed result 0
Number with incorrect result 1725
Number with correct result 8107
[82.5%]
example 0 = {'prompt_text': "mnli hypothesis: Your contributions were of no help with our students' education. premise: Your contribution helped make it possible for us to provide our students with a quality education. target:", 'class_label': 'contradiction'}
result = {'generated_text': ' contradiction'}
First 10 generated_text and expected class_label results:
0: 'contradiction' contradiction
1: 'contradiction' contradiction
2: 'entailment' entailment
3: 'contradiction' contradiction
4: 'entailment' entailment
5: 'entailment' entailment
6: 'contradiction' contradiction
7: 'contradiction' contradiction
8: 'entailment' neutral
9: 'contradiction' contradiction
How to use
The model can be easily loaded using AutoModelForCausalLM. You can use the pipeline API for text generation.
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-j-6B')
hf_model = AutoModelForCausalLM.from_pretrained("Graphcore/gptj-mnli", pad_token_id=tokenizer.eos_token_id)
generator = pipeline('text-generation', model=hf_model, tokenizer=tokenizer)
prompt = "mnli hypothesis: Your contributions were of no help with our students' education." \
"premise: Your contribution helped make it possible for us to provide our students with a quality education. target:"
out = generator(prompt, return_full_text=False, max_new_tokens=5, top_k=1)
# [{'generated_text': ' contradiction'}]
You can create prompt-like inputs for the model using the data_utils.py script provided.
from datasets import load_dataset
from data_utils import form_text, split_text
dataset = load_dataset('glue', 'mnli', split='validation_mismatched')
dataset = dataset.map(
form_text, remove_columns=['hypothesis', 'premise','label', 'idx'])
# dataset[0] {'text': "mnli hypothesis: Your contributions were of no help with our students' education. premise: Your contribution helped make it possible for us to provide our students with a quality education. target: contradiction<|endoftext|>"}
dataset = dataset.map(split_text, remove_columns=['text'])
# dataset[0] {'prompt_text': "mnli hypothesis: Your contributions were of no help with our students' education. premise: Your contribution helped make it possible for us to provide our students with a quality education. target:",
# 'class_label': 'contradiction'}