|
--- |
|
language: |
|
- en |
|
tags: |
|
- text-classification |
|
- zero-shot-classification |
|
metrics: |
|
- accuracy |
|
widget: |
|
- text: "I first thought that I liked the movie, but upon second thought it was actually disappointing. [SEP] The movie was good." |
|
|
|
--- |
|
# DeBERTa-v3-base-mnli-fever-anli |
|
## Model description |
|
This model was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. This base model outperforms almost all large models on the [ANLI benchmark](https://github.com/facebookresearch/anli). |
|
The base model is [DeBERTa-v3-base from Microsoft](https://huggingface.co/microsoft/deberta-v3-base). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see annex 11 of the original [DeBERTa paper](https://arxiv.org/pdf/2006.03654.pdf). |
|
|
|
## Intended uses & limitations |
|
#### How to use the model |
|
```python |
|
from transformers import AutoTokenizer, AutoModelForSequenceClassification |
|
import torch |
|
|
|
model_name = "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli" |
|
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 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-base-mnli-fever-anli was trained on the MultiNLI, Fever-NLI and Adversarial-NLI (ANLI) datasets, which comprise 763 913 NLI hypothesis-premise pairs. |
|
|
|
### Training procedure |
|
DeBERTa-v3-base-mnli-fever-anli was trained using the Hugging Face trainer with the following hyperparameters. |
|
``` |
|
training_args = TrainingArguments( |
|
num_train_epochs=3, # total number of training epochs |
|
learning_rate=2e-05, |
|
per_device_train_batch_size=32, # batch size per device during training |
|
per_device_eval_batch_size=32, # batch size for evaluation |
|
warmup_ratio=0.1, # number of warmup steps for learning rate scheduler |
|
weight_decay=0.06, # strength of weight decay |
|
fp16=True # mixed precision training |
|
) |
|
``` |
|
### Eval results |
|
The model was evaluated using the test sets for MultiNLI and ANLI and the dev set for Fever-NLI. The metric used is accuracy. |
|
|
|
mnli-m | mnli-mm | fever-nli | anli-all | anli-r3 |
|
---------|----------|---------|----------|---------- |
|
0.903 | 0.903 | 0.777 | 0.579 | 0.495 |
|
|
|
## Limitations and bias |
|
Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. |
|
|
|
### BibTeX entry and citation info |
|
If you want to cite this model, please cite the original DeBERTa paper, the respective NLI datasets and include a link to this model on the Hugging Face hub. |
