Update README.md

README.md

@@ -5,7 +5,7 @@ tags: []
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is designed to identify whether two product titles (including specifications) describe the same product. The model generates a score, where a score greater than 0.5 indicates that the products are likely the same.The treshold value can be used as needed. It is based on the BERT base uncased architecture and has been fine-tuned on a custom dataset derived from real-world examples. The model performs particularly well on longer sequences.
 
 
 
@@ -15,23 +15,12 @@ tags: []
 
 <!-- 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 type:** Binary Classification
+- **Language(s) (NLP):** English
+- **Finetuned from model [optional]:** bert-base-uncased
 
-### 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
 
@@ -39,161 +28,67 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+This model can be directly used to determine if two product titles are the same. It is especially useful in e-commerce applications for deduplication, catalog matching, and product comparison.
 
-### 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. -->
+The model is not suitable for tasks requiring deep contextual understanding beyond product titles or for comparing product descriptions that lack specification details.
 
 [More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **Bias**: The model may inherit biases present in the dataset, particularly related to product categories with less representation.
+- **Limitations**: The model is optimized for product titles and may perform poorly on short, ambiguous titles or non-standardized names.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+It is recommended to use this model for products with clearly defined titles and specifications. Users should also monitor performance on specific product categories to identify and address any biases.
+
 
 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
+```python
+import torch
+from transformers import BertTokenizer
+from huggingface_hub import hf_hub_download
 
-#### Testing Data
+# Step 1: Download model.py
+file_path = hf_hub_download(repo_id='Berketarak/Product-Matching-Classifier', filename='model.py')
 
-<!-- This should link to a Dataset Card if possible. -->
+# Step 2: Add directory containing model.py to the Python path
+import sys, os
+model_dir = os.path.dirname(file_path)
+sys.path.append(model_dir)
 
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+# Step 3: Import custom model class
+from model import CustomBertModel
 
-#### Metrics
+# Step 4: Load tokenizer and model
+tokenizer = BertTokenizer.from_pretrained('Berketarak/Product-Matching-Classifier')
+model = CustomBertModel.from_pretrained('Berketarak/Product-Matching-Classifier')
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
 
-#### Summary
+product1 = 'X brand Pegasus Sneakers'
+product2 = 'Y brand Shoes'
 
+# Tokenize the input
+inputs = tokenizer(product1, product2, padding='max_length', truncation=True, max_length=350, return_tensors='pt')
 
+# Inference
+with torch.no_grad():
+    input_ids = inputs['input_ids'].to(device)
+    attention_mask = inputs['attention_mask'].to(device)
+    token_type_ids = inputs['token_type_ids'].to(device)
+    output = model(input_ids, attention_mask, token_type_ids).item()
 
-## 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
+# Interpret the output
+if output > 0.5:
+    print(f"The products are likely the SAME. Model output: {output}")
+else:
+    print(f"The products are likely DIFFERENT. Model output: {output}")
 
-[More Information Needed]