Darshan03
/

AI-Hackathon

@@ -3,197 +3,68 @@ library_name: transformers
 tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## 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
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
 ### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- 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. -->
-[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. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### 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]
-## 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]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 tags: []
 ---
+# Model Card for Fine-Tuned BERT for Classification
+This model is a fine-tuned version of BERT for binary text classification tasks. It was trained on a specific dataset for classification purposes and is intended for use in text classification applications.
 ## Model Details
 ### Model Description
+This BERT model has been fine-tuned for binary text classification. It is based on the `bert-base-uncased` model and has been trained to classify text into two categories: Class 0 and Class 1.
+- **Developed by:** Your Name or Organization
+- **Funded by [optional]:** [Add funding information if applicable]
+- **Shared by [optional]:** [Add sharing information if applicable]
+- **Model type:** Text Classification
+- **Language(s) (NLP):** English
+- **License:** Apache-2.0
+- **Finetuned from model [optional]:** BERT `bert-base-uncased`
+### Model Sources
+- **Repository:** [Link to your GitHub repository if available]
+- **Paper [optional]:** [Link to related paper if available]
+- **Demo [optional]:** [Link to a live demo if available]
 ## Uses
 ### Direct Use
+This model is intended for binary text classification tasks. It can be used to classify text data into two categories.
+### Downstream Use
+The model can be fine-tuned further for other specific binary text classification tasks by using appropriate datasets and training procedures.
 ### Out-of-Scope Use
+The model is not intended for use in tasks other than binary text classification. Misuse includes any application that requires multi-class classification or tasks beyond the scope of text classification.
 ## Bias, Risks, and Limitations
+This model inherits biases present in the pre-trained BERT model and the fine-tuning dataset. Users should be cautious of potential biases related to language, context, and dataset-specific characteristics.
 ### Recommendations
+Users should evaluate the model on their specific tasks and datasets to ensure it performs as expected. It is recommended to perform bias and fairness checks before deploying the model in production.
 ## How to Get Started with the Model
+```python
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+# Load the model and tokenizer
+model = BertForSequenceClassification.from_pretrained('your-username/bert-fine-tuned')
+tokenizer = BertTokenizer.from_pretrained('your-username/bert-fine-tuned')
+# Tokenize the input text
+inputs = tokenizer("Your text here", return_tensors='pt', padding=True, truncation=True, max_length=128)
+# Perform inference
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_class = torch.argmax(logits, dim=1).item()
+print(f"Predicted class: {predicted_class}")