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MoritzLaurer HF staff
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---
language:
- en
tags:
- text-classification
- zero-shot-classification
license: mit
metrics:
- accuracy
datasets:
- multi_nli
- anli
- fever
- lingnli
- alisawuffles/WANLI
#pipeline_tag:
#- text-classification
widget:
- text: "I first thought that I really liked the movie, but upon second thought it was actually disappointing. [SEP] The movie was not good."
model-index: # info: https://github.com/huggingface/hub-docs/blame/main/modelcard.md
- name: DeBERTa-v3-large-mnli-fever-anli-ling-wanli
results:
- task:
type: text-classification # Required. Example: automatic-speech-recognition
name: Natural Language Inference # Optional. Example: Speech Recognition
dataset:
type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: MultiNLI-matched # Required. A pretty name for the dataset. Example: Common Voice (French)
split: validation_matched # Optional. Example: test
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0,912 # Required. Example: 20.90
#name: # Optional. Example: Test WER
verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
- task:
type: text-classification # Required. Example: automatic-speech-recognition
name: Natural Language Inference # Optional. Example: Speech Recognition
dataset:
type: multi_nli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: MultiNLI-mismatched # Required. A pretty name for the dataset. Example: Common Voice (French)
split: validation_mismatched # Optional. Example: test
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0,908 # Required. Example: 20.90
#name: # Optional. Example: Test WER
verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
- task:
type: text-classification # Required. Example: automatic-speech-recognition
name: Natural Language Inference # Optional. Example: Speech Recognition
dataset:
type: anli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: ANLI-all # Required. A pretty name for the dataset. Example: Common Voice (French)
split: test_r1+test_r2+test_r3 # Optional. Example: test
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0,702 # Required. Example: 20.90
#name: # Optional. Example: Test WER
verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
- task:
type: text-classification # Required. Example: automatic-speech-recognition
name: Natural Language Inference # Optional. Example: Speech Recognition
dataset:
type: anli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: ANLI-r3 # Required. A pretty name for the dataset. Example: Common Voice (French)
split: test_r3 # Optional. Example: test
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0,64 # Required. Example: 20.90
#name: # Optional. Example: Test WER
verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
- task:
type: text-classification # Required. Example: automatic-speech-recognition
name: Natural Language Inference # Optional. Example: Speech Recognition
dataset:
type: alisawuffles/WANLI # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: WANLI # Required. A pretty name for the dataset. Example: Common Voice (French)
split: test # Optional. Example: test
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0,77 # Required. Example: 20.90
#name: # Optional. Example: Test WER
verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
- task:
type: text-classification # Required. Example: automatic-speech-recognition
name: Natural Language Inference # Optional. Example: Speech Recognition
dataset:
type: lingnli # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: LingNLI # Required. A pretty name for the dataset. Example: Common Voice (French)
split: test # Optional. Example: test
metrics:
- type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0,87 # Required. Example: 20.90
#name: # Optional. Example: Test WER
verified: false # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
---
# DeBERTa-v3-large-mnli-fever-anli-ling-wanli
## Model description
This model was fine-tuned on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) datasets, which comprise 885 242 NLI hypothesis-premise pairs. This model is the best performing NLI model on the Hugging Face Hub as of 06.06.22 and can be used for zero-shot classification. It significantly outperforms all other large models on the [ANLI benchmark](https://github.com/facebookresearch/anli).
The foundation model is [DeBERTa-v3-large from Microsoft](https://huggingface.co/microsoft/deberta-v3-large). Released on 06.12.21, DeBERTa-v3-large is currently the best large-sized foundation model for text classification. It combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the [paper](https://arxiv.org/abs/2111.09543)
## Intended uses & limitations
#### How to use the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
hypothesis = "The movie was not good."
input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu"
prediction = torch.softmax(output["logits"][0], -1).tolist()
label_names = ["entailment", "neutral", "contradiction"]
prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
print(prediction)
```
### Training data
DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained on the [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), Adversarial-NLI ([ANLI](https://huggingface.co/datasets/anli)), [LingNLI](https://arxiv.org/pdf/2104.07179.pdf) and [WANLI](https://huggingface.co/datasets/alisawuffles/WANLI) datasets, which comprise 885 242 NLI hypothesis-premise pairs. Note that [SNLI](https://huggingface.co/datasets/snli) was explicitly excluded due to quality issues with the dataset. More data does not necessarily make for better NLI models.
### Training procedure
DeBERTa-v3-large-mnli-fever-anli-ling-wanli was trained using the Hugging Face trainer with the following hyperparameters. Note that longer training with more epochs hurt performance in my tests (overfitting).
```
training_args = TrainingArguments(
num_train_epochs=4, # total number of training epochs
learning_rate=5e-06,
per_device_train_batch_size=16, # batch size per device during training
gradient_accumulation_steps=2, # doubles the effective batch_size to 32, while decreasing memory requirements
per_device_eval_batch_size=64, # batch size for evaluation
warmup_ratio=0.06, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
fp16=True # mixed precision training
)
```
### Eval results
The model was evaluated using the test sets for MultiNLI, ANLI, LingNLI, WANLI and the dev set for Fever-NLI. The metric used is accuracy.
The model achieves state-of-the-art performance on each dataset. Surprisingly, it outperforms the previous [state-of-the-art on ANLI](https://github.com/facebookresearch/anli) (ALBERT-XXL) by 8,3%. I assume that this is because ANLI was created to fool masked language models like RoBERTa (or ALBERT), while DeBERTa-v3 uses a better pre-training objective (RTD), disentangled attention and I fine-tuned it on higher quality NLI data.
|Datasets|mnli_test_m|mnli_test_mm|anli_test|anli_test_r3|ling_test|wanli_test|
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|Accuracy|0.912|0.908|0.702|0.64|0.87|0.77|
|Speed (text/sec, A100 GPU)|696.0|697.0|488.0|425.0|828.0|980.0|
## Limitations and bias
Please consult the original DeBERTa-v3 paper and literature on different NLI datasets for more information on the training data and potential biases. The model will reproduce statistical patterns in the training data.
### BibTeX entry and citation info
If you want to cite this model, please cite my [preprint on low-resource text classification](https://osf.io/74b8k/) and the original DeBERTa-v3 paper.
### Ideas for cooperation or questions?
If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
### Debugging and issues
Note that DeBERTa-v3 was released on 06.12.21 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 might solve some issues.