readme: ---
  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
  
  