distilbert-base-uncased-mnli / README.md

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	---
	language: en
	pipeline_tag: zero-shot-classification
	tags:
	- distilbert
	datasets:
	- multi_nli
	metrics:
	- accuracy
	---

	# DistilBERT base model (uncased)


	## Table of Contents
	- [Model Details](#model-details)
	- [How to Get Started With the Model](#how-to-get-started-with-the-model)
	- [Uses](#uses)
	- [Risks, Limitations and Biases](#risks-limitations-and-biases)
	- [Training](#training)
	- [Evaluation](#evaluation)
	- [Environmental Impact](#environmental-impact)



	## Model Details
	Model Description: 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.
	- Developed by: The [Typeform](https://www.typeform.com/) team.
	- Model Type: Zero-Shot Classification
	- Language(s): English
	- License: Unknown
	- Parent Model: See the [distilbert base uncased model](https://huggingface.co/distilbert-base-uncased) for more information about the Distilled-BERT base model.


	## How to Get Started with the Model

	```python
	from transformers import AutoTokenizer, AutoModelForSequenceClassification

	tokenizer = AutoTokenizer.from_pretrained("typeform/distilbert-base-uncased-mnli")

	model = AutoModelForSequenceClassification.from_pretrained("typeform/distilbert-base-uncased-mnli")

	```

	## Uses
	This model can be used for text classification tasks.


	## Risks, Limitations and Biases
	CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.

	Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).


	## Training

	#### Training Data


	This model of DistilBERT-uncased is pretrained on the Multi-Genre Natural Language Inference [(MultiNLI)](https://huggingface.co/datasets/multi_nli) corpus. It is a crowd-sourced collection of 433k sentence pairs annotated with textual entailment information. The corpus covers a range of genres of spoken and written text, and supports a distinctive cross-genre generalization evaluation.

	This model is also not case-sensitive, i.e., it does not make a difference between "english" and "English".


	#### Training Procedure

	Training is done on a [p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) AWS EC2 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


	#### Evaluation Results
	When fine-tuned on downstream tasks, this model achieves the following results:
	- Epoch = 5.0
	- Evaluation Accuracy = 0.8206875508543532
	- Evaluation Loss = 0.8706700205802917
	- Evaluation Runtime = 17.8278
	- Evaluation Samples per second = 551.498

	MNLI and MNLI-mm results:

	\| Task \| MNLI \| MNLI-mm \|
	\|:----:\|:----:\|:----:\|
	\| \| 82.0 \| 82.0 \|



	## Environmental Impact

	Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type based on the [associated paper](https://arxiv.org/pdf/2105.09680.pdf).


	Hardware Type: 1 NVIDIA Tesla V100 GPUs

	Hours used: Unknown

	Cloud Provider: AWS EC2 P3


	Compute Region: Unknown



	Carbon Emitted: (Power consumption x Time x Carbon produced based on location of power grid): Unknown