Model Card

Hi!👋 This PR has a some additional information for the model card, based on the format we are using as part of our effort to standardise model cards at Hugging Face. Feel free to merge if you are ok with the changes! (cc

@Marissa



@Meg



@Nazneen

)

README.md

@@ -11,11 +11,61 @@ metrics:
 
 # DistilBERT base model (uncased)
 
-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".
+
+## 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 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:
+#### 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 \
@@ -30,8 +80,40 @@ $ run_glue.py \
     --output_dir /tmp/distilbert-base-uncased_mnli/
 ```
 
-## Evaluation results
+## 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 |
+|      | 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
+