Update README.md

README.md

@@ -1,3 +1,79 @@
----
-license: apache-2.0
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+---
+license: apache-2.0
+language:
+- en
+library_name: bertopic
+tags:
+- topic-sentiment
+---
+
+## DeBERTa-v3-large-mnli-fever-anli-ling-wanli
+
+Model description
+This model was fine-tuned on the MultiNLI, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and 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.
+
+The foundation model is DeBERTa-v3-large from Microsoft. DeBERTa-v3 combines several recent innovations compared to classical Masked Language Models like BERT, RoBERTa etc., see the paper
+
+How to use the model
+Simple zero-shot classification pipeline
+from transformers import pipeline
+classifier = pipeline("zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli")
+sequence_to_classify = "Angela Merkel is a politician in Germany and leader of the CDU"
+candidate_labels = ["politics", "economy", "entertainment", "environment"]
+output = classifier(sequence_to_classify, candidate_labels, multi_label=False)
+print(output)
+
+NLI use-case
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+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, Fever-NLI, Adversarial-NLI (ANLI), LingNLI and WANLI datasets, which comprise 885 242 NLI hypothesis-premise pairs. Note that 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 (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.
+
+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?
+If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or LinkedIn
+
+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.
+