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ambiguity-aware-roberta-mnli
hancheolp/ambiguity-aware-roberta-mnli
ambiguity-aware-roberta-mnli is a machine learning model from hancheolp. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This model is trained on a subset of the MNLI dataset and is capable of representing the ambiguity occurring in natural language inference tasks as an accurate distribution (i.e., softmax output). It was introduced in…
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At a glance
- License
- mit
- Model type
- roberta
- Access
- Public
- Created
- Sep 16, 2024
- Updated
- Sep 16, 2024
- SHA
- e2def950
Base models
Model card
From the Hugging Face model README
Ambiguity-aware RoBERTa
This model is trained on a subset of the MNLI dataset and is capable of representing the ambiguity occurring in natural language inference tasks as an accurate distribution (i.e., softmax output). It was introduced in the following paper: "Deep Model Compression Also Helps Models Capture Ambiguity" (ACL 2023).
Usage
from transformers import RobertaTokenizer, RobertaForSequenceClassification
tokenizer = RobertaTokenizer.from_pretrained('hancheolp/ambiguity-aware-roberta-snli')
model = RobertaForSequenceClassification.from_pretrained('hancheolp/ambiguity-aware-roberta-snli')
premise = "To the sociologists' speculations, add mine."
hypothesis = "I don't agree with sociologists."
encoded_input = tokenizer(premise, hypothesis, return_tensors='pt')
output = model(**encoded_input)
distribution = output.logits.softmax(dim=-1)
Each index of the output vector represents the following:
- 0: entailment
- 1: neutral
- 2: contradiction
- Type
- roberta
- License
- mit
- Languages
- en
- Created
- Sep 16, 2024
- Updated
- Sep 16, 2024