readme: ---
  language: en
  pipeline_tag: zero-shot-classification
  tags:
  - distilbert
  datasets:
  - multi_nli
  metrics:
  - accuracy
  ---
  
  # ONNX convert typeform/distilbert-base-uncased-mnli
  
  ## Conversion of [typeform/distilbert-base-uncased-mnli](typeform/distilbert-base-uncased-mnli)
  
  
  
  This is the [uncased DistilBERT model](https://huggingface.co/distilbert-base-uncased) fine-tuned on [Multi-Genre Natural Language Inference](https://huggingface.co/datasets/multi_nli) (MNLI) dataset for the zero-shot classification task. The model is not case-sensitive, i.e., it does not make a difference between "english" and "English".
  
  ## Training
  
  Training is done on a [p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) AWS EC2 instance (1 NVIDIA Tesla V100 GPUs), with the following hyperparameters:
  
  ```
  $ run_glue.py \
      --model_name_or_path distilbert-base-uncased \
      --task_name mnli \
      --do_train \
      --do_eval \
      --max_seq_length 128 \
      --per_device_train_batch_size 16 \
      --learning_rate 2e-5 \
      --num_train_epochs 5 \
      --output_dir /tmp/distilbert-base-uncased_mnli/
  ```
  
  ## Evaluation results
  
  | Task | MNLI | MNLI-mm |
  |:----:|:----:|:----:|
  |      | 82.0 | 82.0 |