Create README.md

README.md

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+---
+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 |