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--- |
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language: |
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- en |
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tags: |
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- text-classification |
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- zero-shot-classification |
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metrics: |
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- accuracy |
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pipeline_tag: zero-shot-classification |
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--- |
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# DeBERTa-v3-base-mnli-fever-anli |
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## Model description |
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This model was trained on the MultiNLI dataset, which consists of 392 702 NLI hypothesis-premise pairs. |
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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). For a more powerful model, check out [DeBERTa-v3-base-mnli-fever-anli](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli) which was trained on even more data. |
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## Intended uses & limitations |
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#### How to use the model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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import torch |
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model_name = "MoritzLaurer/DeBERTa-v3-base-mnli" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing." |
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hypothesis = "The movie was good." |
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input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") |
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output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" |
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prediction = torch.softmax(output["logits"][0], -1).tolist() |
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label_names = ["entailment", "neutral", "contradiction"] |
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prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} |
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print(prediction) |
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``` |
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### Training data |
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This model was trained on the MultiNLI dataset, which consists of 392 702 NLI hypothesis-premise pairs. |
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### Training procedure |
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DeBERTa-v3-base-mnli was trained using the Hugging Face trainer with the following hyperparameters. |
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``` |
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training_args = TrainingArguments( |
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num_train_epochs=5, # total number of training epochs |
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learning_rate=2e-05, |
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per_device_train_batch_size=32, # batch size per device during training |
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per_device_eval_batch_size=32, # batch size for evaluation |
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warmup_ratio=0.1, # number of warmup steps for learning rate scheduler |
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weight_decay=0.06, # strength of weight decay |
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fp16=True # mixed precision training |
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) |
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``` |
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### Eval results |
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The model was evaluated using the matched test set and achieves 0.90 accuracy. |
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## Limitations and bias |
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Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. |
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### BibTeX entry and citation info |
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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. |
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### Ideas for cooperation or questions? |
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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/) |
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### Debugging and issues |
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Note that DeBERTa-v3 was released recently 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. |
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## Model Recycling |
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[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=0.97&mnli_lp=nan&20_newsgroup=-0.39&ag_news=0.19&amazon_reviews_multi=0.10&anli=1.31&boolq=0.81&cb=8.93&cola=0.01&copa=13.60&dbpedia=-0.23&esnli=-0.51&financial_phrasebank=0.61&imdb=-0.26&isear=-0.35&mnli=-0.34&mrpc=1.24&multirc=1.50&poem_sentiment=-0.19&qnli=0.30&qqp=0.13&rotten_tomatoes=-0.55&rte=3.57&sst2=0.35&sst_5bins=0.39&stsb=1.10&trec_coarse=-0.36&trec_fine=-0.02&tweet_ev_emoji=1.11&tweet_ev_emotion=-0.35&tweet_ev_hate=1.43&tweet_ev_irony=-2.65&tweet_ev_offensive=-1.69&tweet_ev_sentiment=-1.51&wic=0.57&wnli=-2.61&wsc=9.95&yahoo_answers=-0.33&model_name=MoritzLaurer%2FDeBERTa-v3-base-mnli&base_name=microsoft%2Fdeberta-v3-base) using MoritzLaurer/DeBERTa-v3-base-mnli as a base model yields average score of 80.01 in comparison to 79.04 by microsoft/deberta-v3-base. |
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The model is ranked 1st among all tested models for the microsoft/deberta-v3-base architecture as of 09/01/2023 |
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Results: |
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| 20_newsgroup | ag_news | amazon_reviews_multi | anli | boolq | cb | cola | copa | dbpedia | esnli | financial_phrasebank | imdb | isear | mnli | mrpc | multirc | poem_sentiment | qnli | qqp | rotten_tomatoes | rte | sst2 | sst_5bins | stsb | trec_coarse | trec_fine | tweet_ev_emoji | tweet_ev_emotion | tweet_ev_hate | tweet_ev_irony | tweet_ev_offensive | tweet_ev_sentiment | wic | wnli | wsc | yahoo_answers | |
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|---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|-------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:| |
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| 86.0196 | 90.6333 | 66.96 | 60.0938 | 83.792 | 83.9286 | 86.5772 | 72 | 79.2 | 91.419 | 85.1 | 94.232 | 71.5124 | 89.4426 | 90.4412 | 63.7583 | 86.5385 | 93.8129 | 91.9144 | 89.8687 | 85.9206 | 95.4128 | 57.3756 | 91.377 | 97.4 | 91 | 47.302 | 83.6031 | 57.6431 | 77.1684 | 83.3721 | 70.2947 | 71.7868 | 67.6056 | 74.0385 | 71.7 | |
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For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/) |
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