Create README.md

README.md

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+---
+language: 
+- en
+tags:
+- text-classification
+- zero-shot-classification
+metrics:
+- accuracy
+widget:
+- text: "I first thought that I liked the movie, but upon second thought the movie was actually disappointing. [SEP] The movie was good."
+
+---
+# MiniLM-L6-mnli-fever-docnli-ling-2c
+## Model description
+This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [DocNLI](https://arxiv.org/pdf/2106.09449.pdf) (which includes [ANLI](https://github.com/facebookresearch/anli), QNLI, DUC, CNN/DailyMail, Curation). 
+
+It is the only model in the model hub trained on 8 NLI datasets, including DocNLI with very long texts to learn long range reasoning. Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". The DocNLI merges the classes "neural" and "contradiction" into "not-entailment" to create more training data. 
+
+The base model is MiniLM-L6 from Microsoft. Which is very fast, but a bit less accurate than other models.  
+
+## Intended uses & limitations
+#### How to use the model
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name = "MoritzLaurer/MiniLM-L6-mnli-fever-docnli-ling-2c"
+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
+This model was trained on 1.279.665 hypothesis-premise pairs from 8 NLI datasets: [MultiNLI](https://huggingface.co/datasets/multi_nli), [Fever-NLI](https://github.com/easonnie/combine-FEVER-NSMN/blob/master/other_resources/nli_fever.md), [LingNLI](https://arxiv.org/abs/2104.07179) and [DocNLI](https://arxiv.org/pdf/2106.09449.pdf) (which includes [ANLI](https://github.com/facebookresearch/anli), QNLI, DUC, CNN/DailyMail, Curation).
+
+### Training procedure
+MiniLM-L6-mnli-fever-docnli-ling-2c 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 binary test sets for MultiNLI and ANLI and the binary dev set for Fever-NLI. The metric used is accuracy.
+
+mnli-m | mnli-mm | fever-nli | anli-all | anli-r3
+---------|----------|---------|----------|----------
+(to upload)
+
+
+## Limitations and bias
+Please consult the original MiniLM 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 MiniLM paper, the respective NLI datasets and include a link to this model on the Hugging Face hub.