Model Card (#2)

- Model Card (c213a4b99cbb618477949c1483b6916d023c2e45)


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

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
+