Updating model card

README.md

@@ -5,6 +5,10 @@ language:
 - en
 base_model:
 - FacebookAI/roberta-base
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: transformers
 ---
 # Model Card for Model ID
 
@@ -20,9 +24,8 @@ This modelcard aims to be a base template for new models. It has been generated
 
 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
+- **Developed by:** ESIEA Students 
+- **Shared by [optional]:** ESIEA Students
 - **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
@@ -40,6 +43,8 @@ This modelcard aims to be a base template for new models. It has been generated
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
+The model can be used to classify text based on their toxicities
+
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
@@ -82,14 +87,21 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
+We did train the model on the googleJigSaw toxic dataset as mentionned above on the 150k comments
+
+
 [More Information Needed]
 
 ### Training Procedure
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
+we trained
+
 #### Preprocessing [optional]
 
+we only did some basic data-cleaning
+
 [More Information Needed]
 
 
@@ -101,6 +113,8 @@ Use the code below to get started with the model.
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
+training time 4hours on a gtx 1050TI GPU on 3 epochs
+
 [More Information Needed]
 
 ## Evaluation
@@ -125,6 +139,8 @@ Use the code below to get started with the model.
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
+Accuracy of : **90%**
+
 [More Information Needed]
 
 ### Results
@@ -147,11 +163,8 @@ Use the code below to get started with the model.
 
 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).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Hardware Type:** GTX 1050 TI
+- **Hours used:** 4 HOURS
 
 ## Technical Specifications [optional]
 
@@ -171,6 +184,8 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 [More Information Needed]
 
+We did use torch
+
 ## Citation [optional]
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->