readme: ---
  language: 
  - en
  tags:
  - text-classification
  - zero-shot-classification
  metrics:
  - accuracy
  datasets:
  - multi_nli
  - anli
  - fever
  - lingnli
  pipeline_tag: zero-shot-classification
  
  ---
  # xtremedistil-l6-h256-mnli-fever-anli-ling-binary
  ## Model description
  This model was trained on 782 357 hypothesis-premise pairs from 4 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 [ANLI](https://github.com/facebookresearch/anli). 
  
  Note that the model was trained on binary NLI to predict either "entailment" or "not-entailment". This is specifically designed for zero-shot classification, where the difference between "neutral" and "contradiction" is irrelevant. 
  
  The base model is [xtremedistil-l6-h256-uncased from Microsoft](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased). 
  
  ## Intended uses & limitations
  #### How to use the model
  ```python
  from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import torch
  
  model_name = "MoritzLaurer/xtremedistil-l6-h256-mnli-fever-anli-ling-binary"
  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", "not_entailment"]
  prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
  print(prediction)
  ```
  ### Training data
  This model was trained on 782 357 hypothesis-premise pairs from 4 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 [ANLI](https://github.com/facebookresearch/anli). 
  
  ### Training procedure
  xtremedistil-l6-h256-mnli-fever-anli-ling-binary was trained using the Hugging Face trainer with the following hyperparameters.
  ```
  training_args = TrainingArguments(
      num_train_epochs=5,              # 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, ANLI, LingNLI and the binary dev set for Fever-NLI (two classes instead of three). The metric used is accuracy.
  
  dataset | mnli-m-2c | mnli-mm-2c | fever-nli-2c | anli-all-2c | anli-r3-2c | lingnli-2c
  --------|---------|----------|---------|----------|----------|------
  accuracy | 0.897 | 0.898 | 0.861 | 0.607 | 0.62 | 0.827
  speed (text/sec, GPU Tesla P100, 128 batch) | 1490 | 1485 | 760 | 1186 | 1062 | 1791
  
  
  ## Limitations and bias
  Please consult the original 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 paper, the respective NLI datasets and include a link to this model on the Hugging Face hub. 
  
  ### 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 the model 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. 