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Model Card (#2)

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- Model Card (c213a4b99cbb618477949c1483b6916d023c2e45)


Co-authored-by: Ezi Ozoani <Ezi@users.noreply.huggingface.co>

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  # DistilBERT base model (uncased)
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- 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".
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training
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- 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:
 
 
 
 
 
 
 
 
 
 
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  ```
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  $ run_glue.py \
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  --output_dir /tmp/distilbert-base-uncased_mnli/
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  ```
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- ## Evaluation results
 
 
 
 
 
 
 
 
 
 
 
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  | Task | MNLI | MNLI-mm |
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  |:----:|:----:|:----:|
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- | | 82.0 | 82.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # DistilBERT base model (uncased)
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+
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+ ## Table of Contents
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+ - [Model Details](#model-details)
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+ - [How to Get Started With the Model](#how-to-get-started-with-the-model)
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+ - [Uses](#uses)
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+ - [Risks, Limitations and Biases](#risks-limitations-and-biases)
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+ - [Training](#training)
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+ - [Evaluation](#evaluation)
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+ - [Environmental Impact](#environmental-impact)
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+
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+ ## Model Details
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+ **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.
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+ - **Developed by:** The [Typeform](https://www.typeform.com/) team.
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+ - **Model Type:** Zero-Shot Classification
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+ - **Language(s):** English
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+ - **License:** Unknown
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+ - **Parent Model:** See the [distilbert base uncased model](https://huggingface.co/distilbert-base-uncased) for more information about the Distilled-BERT base model.
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+ ## How to Get Started with the Model
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForSequenceClassification
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+ tokenizer = AutoTokenizer.from_pretrained("typeform/distilbert-base-uncased-mnli")
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+ model = AutoModelForSequenceClassification.from_pretrained("typeform/distilbert-base-uncased-mnli")
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+
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+ ```
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+
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+ ## Uses
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+ This model can be used for text classification tasks.
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+ ## Risks, Limitations and Biases
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+ **CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
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+
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+ 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)).
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+
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  ## Training
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+ #### Training Data
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+ 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.
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+ This model is also **not** case-sensitive, i.e., it does not make a difference between "english" and "English".
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+ #### Training Procedure
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+ Training is done on a [p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) AWS EC2 with the following hyperparameters:
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  ```
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  $ run_glue.py \
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  --output_dir /tmp/distilbert-base-uncased_mnli/
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  ```
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+ ## Evaluation
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+
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+ #### Evaluation Results
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+ When fine-tuned on downstream tasks, this model achieves the following results:
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+ - **Epoch = ** 5.0
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+ - **Evaluation Accuracy =** 0.8206875508543532
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+ - **Evaluation Loss =** 0.8706700205802917
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+ - ** Evaluation Runtime = ** 17.8278
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+ - ** Evaluation Samples per second = ** 551.498
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+ MNLI and MNLI-mm results:
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  | Task | MNLI | MNLI-mm |
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  |:----:|:----:|:----:|
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+ | | 82.0 | 82.0 |
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+ ## Environmental Impact
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+ 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).
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+ **Hardware Type:** 1 NVIDIA Tesla V100 GPUs
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+ **Hours used:** Unknown
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+ **Cloud Provider:** AWS EC2 P3
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+ **Compute Region:** Unknown
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+ **Carbon Emitted:** (Power consumption x Time x Carbon produced based on location of power grid): Unknown
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+