Model Card for DeBERTa-v3-small-tasksource-nli
DeBERTa-v3-small with context length of 1680 tokens fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli).
Training data include HelpSteer v1/v2, logical reasoning tasks (FOLIO, FOL-nli, LogicNLI...), OASST, hh/rlhf, linguistics oriented NLI tasks, tasksource-dpo, fact verification tasks.
This model is suitable for long context NLI or as a backbone for reward models or classifiers fine-tuning.
This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for:
- Zero-shot entailment-based classification for arbitrary labels [ZS].
- Natural language inference [NLI]
- Further fine-tuning on a new task or tasksource task (classification, token classification or multiple-choice) [FT].
| test_name |
accuracy |
| anli/a1 |
57.2 |
| anli/a2 |
46.1 |
| anli/a3 |
47.2 |
| nli_fever |
71.7 |
| FOLIO |
47.1 |
| ConTRoL-nli |
52.2 |
| cladder |
52.8 |
| zero-shot-label-nli |
70.0 |
| chatbot_arena_conversations |
67.8 |
| oasst2_pairwise_rlhf_reward |
75.6 |
| doc-nli |
75.0 |
Zero-shot GPT-4 scores 61% on FOLIO (logical reasoning), 62% on cladder (probabilistic reasoning) and 56.4% on ConTRoL (long context NLI).
[ZS] Zero-shot classification pipeline
from transformers import pipeline
classifier = pipeline("zero-shot-classification",model="tasksource/deberta-small-long-nli")
text = "one day I will see the world"
candidate_labels = ['travel', 'cooking', 'dancing']
classifier(text, candidate_labels)
NLI training data of this model includes label-nli, a NLI dataset specially constructed to improve this kind of zero-shot classification.
[NLI] Natural language inference pipeline
from transformers import pipeline
pipe = pipeline("text-classification",model="tasksource/deberta-small-long-nli")
pipe([dict(text='there is a cat',
text_pair='there is a black cat')]) #list of (premise,hypothesis)
# [{'label': 'neutral', 'score': 0.9952911138534546}]
[FT] Tasknet: 3 lines fine-tuning
# !pip install tasknet
import tasknet as tn
hparams=dict(model_name='tasksource/deberta-small-long-nli', learning_rate=2e-5)
model, trainer = tn.Model_Trainer([tn.AutoTask("glue/rte")], hparams)
trainer.train()
Software and training details
The model was trained on 600 tasks for 250k steps with a batch size of 384 and a peak learning rate of 2e-5. Training took 14 days on Nvidia A30 24GB gpu.
This is the shared model with the MNLI classifier on top. Each task had a specific CLS embedding, which is dropped 10% of the time to facilitate model use without it. All multiple-choice model used the same classification layers. For classification tasks, models shared weights if their labels matched.
https://github.com/sileod/tasksource/ \
https://github.com/sileod/tasknet/ \
Training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing
Citation
More details on this article:
@inproceedings{sileo-2024-tasksource,
title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
author = "Sileo, Damien",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1361",
pages = "15655--15684",
}
Model Card Contact
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