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README.md

@@ -14,9 +14,8 @@ language:
 - th 
 - tr 
 - ur 
-- vi 
-- zh
-license: mit
+- vu 
+- zh 
 tags:
 - zero-shot-classification
 - text-classification
@@ -34,31 +33,15 @@ widget:
 ---
 # Multilingual mDeBERTa-v3-base-mnli-xnli
 ## Model description
-This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual 
-zero-shot classification. The underlying model was pre-trained by Microsoft on the 
-[CC100 multilingual dataset](https://huggingface.co/datasets/cc100). It was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which contains hypothesis-premise pairs from 15 languages, as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli).
-As of December 2021, mDeBERTa-base is the best performing multilingual base-sized transformer model, 
-introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf). 
+This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying model was pre-trained by Microsoft on the [CC100 multilingual dataset](https://huggingface.co/datasets/cc100). It was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which contains hypothesis-premise pairs from 15 languages, as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli).
+As of December 2021, mDeBERTa-base is the best performing multilingual base-sized transformer model, introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf). 
 
-If you are looking for a smaller, faster (but less performant) model, you can 
-try [multilingual-MiniLMv2-L6-mnli-xnli](https://huggingface.co/MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli).
 
-### How to use the model
-#### Simple zero-shot classification pipeline
-```python
-from transformers import pipeline
-classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")
-
-sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU"
-candidate_labels = ["politics", "economy", "entertainment", "environment"]
-output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
-print(output)
-```
-#### NLI use-case
+## Intended uses & limitations
+#### How to use the model
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
-device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
 
 model_name = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -70,8 +53,10 @@ hypothesis = "Emmanuel Macron is the President of France"
 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)
 ```
 
@@ -95,18 +80,18 @@ The model was evaluated on the XNLI test set on 15 languages (5010 texts per lan
 
 Also note that if other multilingual models on the model hub claim performance of around 90% on languages other than English, the authors have most likely made a mistake during testing since non of the latest papers shows a multilingual average performance of more than a few points above 80% on XNLI (see [here](https://arxiv.org/pdf/2111.09543.pdf) or [here](https://arxiv.org/pdf/1911.02116.pdf)). 
 
-average | ar | bg | de | el | en | es | fr | hi | ru | sw | th | tr | ur | vi | zh 
+average | ar | bg | de | el | en | es | fr | hi | ru | sw | th | tr | ur | vu | zh 
 ---------|----------|---------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------
 0.808 | 0.802 | 0.829 | 0.825 | 0.826 | 0.883 | 0.845 | 0.834 | 0.771 | 0.813 | 0.748 | 0.793 | 0.807 | 0.740 | 0.795 | 0.8116
 
+
 ## Limitations and bias
 Please consult the original DeBERTa-V3 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. 
 
-## Citation
-If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k.
-
-## Ideas for cooperation or questions?
+### 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 in late 2021 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 or higher might solve some issues. Note that mDeBERTa currently does not support FP16, see here: https://github.com/microsoft/DeBERTa/issues/77
+### Debugging and issues
+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. Note that mDeBERTa currently does not support FP16, see here: https://github.com/microsoft/DeBERTa/issues/77