deberta-v3-base with context length of 1280 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 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, reward modeling or multiple-choice) [FT].
dataset
accuracy
anli/a1
63.3
anli/a2
47.2
anli/a3
49.4
nli_fever
79.4
FOLIO
61.8
ConTRoL-nli
63.3
cladder
71.1
zero-shot-label-nli
74.4
chatbot_arena_conversations
72.2
oasst2_pairwise_rlhf_reward
73.9
doc-nli
90.0
Zero-shot GPT-4 scores 61% on FOLIO (logical reasoning), 62% on cladder (probabilistic reasoning) and 56.4% on ConTRoL (long context NLI).
from transformers import pipeline
classifier = pipeline("zero-shot-classification",model="tasksource/deberta-base-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.
from transformers import pipeline
pipe = pipeline("text-classification",model="tasksource/deberta-base-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}]
[TA] Tasksource-adapters: 1 line access to hundreds of tasks#
# !pip install tasknet
import tasknet as tn
pipe = tn.load_pipeline('tasksource/deberta-base-long-nli','glue/sst2') # works for 500+ tasksource tasks
pipe(['That movie was great !', 'Awful movie.'])
# [{'label': 'positive', 'score': 0.9956}, {'label': 'negative', 'score': 0.9967}]
The list of tasks is available in model config.json. This is more efficient than ZS since it requires only one forward pass per example, but it is less flexible.
@inproceedings{sileo-2024-tasksource,
title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework",
author = "Sileo, Damien",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
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",
}
Files
9 files · 749 MBWeights 738 MB (safetensors). Sizes and sha256 from the signed manifest.
