Update README.md

README.md

@@ -1,199 +1,126 @@
 ---
-library_name: transformers
-tags: []
+language: 
+- en
+tags:
+- sentiment-analysis
+- text-classification
+- transformers
+- distilbert
+datasets:
+- fizzbuzz/cleaned-toxic-comments
+model-index:
+- name: DistilBERT Sentiment Classifier
+  results:
+  - task:
+      type: text-classification
+      name: Sentiment Analysis
+    dataset:
+      name: Cleaned IMDB Reviews (Kaggle)
+      type: text
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 0.93
+    - name: F1
+      type: f1
+      value: 0.93
+    - name: Precision
+      type: precision
+      value: 0.93
+    - name: Recall
+      type: recall
+      value: 0.93
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
+# DistilBERT Sentiment Classifier
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
+ - Model Type: Transformer-based classifier (DistilBERT)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+ - Base Model: distilbert-base-uncased
 
-[More Information Needed]
+ - Language: English
 
-### Downstream Use [optional]
+ - Task: Sentiment Analysis (binary classification)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+**Labels:**
 
-[More Information Needed]
+0 → Negative
 
-### Out-of-Scope Use
+1 → Positive
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Framework: Hugging Face Transformers
 
-[More Information Needed]
+## Intended Uses & Limitations
 
-## Bias, Risks, and Limitations
+#### Intended Use:
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Sentiment classification of English reviews, comments, or feedback.
 
-[More Information Needed]
+Not Intended Use:
 
-### Recommendations
+Other languages.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Multi-label sentiment tasks (neutral/mixed).
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## ⚠️ Limitations:
 
-## How to Get Started with the Model
+ - May not generalize well outside movie/review-style data.
 
-Use the code below to get started with the model.
+ - Training data may contain cultural and linguistic bias.
 
-[More Information Needed]
+## Training Dataset
 
-## Training Details
+ - Source: Kaggle Cleaned IMDB Reviews Dataset
 
-### Training Data
+ - Size: ~50,000 reviews
 
-<!-- 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. -->
+ - Classes: positive, negative
 
-[More Information Needed]
+ - Converted to integers: positive → 1, negative → 0
 
-### Training Procedure
+## Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+ - Epochs: 3
 
-#### Preprocessing [optional]
+ - Batch Size: 16
 
-[More Information Needed]
+ - Optimizer: AdamW
 
+ - Learning Rate: 5e-5
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+ - Framework: Hugging Face Trainer API
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## 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. -->
-
-**BibTeX:**
-
-[More Information Needed]
+The model was tested on a held-out validation set of 9,917 reviews.
 
-**APA:**
+Class	Precision	Recall	F1-score	Support
+Negative (0)	0.93	0.93	0.93	4,939
+Positive (1)	0.93	0.93	0.93	4,978
 
-[More Information Needed]
+## Overall
 
-## Glossary [optional]
+ - Accuracy: 93%
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+ - Macro Avg F1: 0.93
 
-[More Information Needed]
+ - Weighted Avg F1: 0.93
 
-## More Information [optional]
 
-[More Information Needed]
+## How to Use
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 
-## Model Card Authors [optional]
+model_name = "your-username/distilbert-sentiment-classifier"
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-## Model Card Contact
+nlp = pipeline("text-classification", model=model, tokenizer=tokenizer)
 
-[More Information Needed]
+print(nlp("I really loved this movie, it was amazing!"))
+```
+```
+# [{'label': 'POSITIVE', 'score': 0.98}]
+```