Update README.md

README.md

@@ -1,199 +1,81 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+language:
+- tr
+metrics:
+- accuracy
+- f1
+base_model:
+- dbmdz/distilbert-base-turkish-cased
+pipeline_tag: text-classification
 ---
+# byunal/distilbert-base-turkish-cased-stance
 
-# Model Card for Model ID
+![Model card](https://huggingface.co/front/assets/huggingface_logo.svg)
 
-<!-- Provide a quick summary of what the model is/does. -->
+This repository contains a fine-tuned BERT model for stance detection in Turkish. The base model for this fine-tuning is [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased). The model has been specifically trained on a uniquely collected Turkish stance detection dataset.
 
+## Model Description
 
+- **Model Name**: byunal/distilbert-base-turkish-cased-stance
+- **Base Model**: [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased)
+- **Task**: Stance Detection
+- **Language**: Turkish
 
-## Model Details
+The model predicts the stance of a given text towards a specific target. Possible stance labels include:
 
-### Model Description
+- **Favor**: The text supports the target
+- **Against**: The text opposes the target
+- **Neutral**: The text does not express a clear stance on the target
 
-<!-- Provide a longer summary of what this model is. -->
+## Installation
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+To install the necessary libraries and load the model, run:
 
-- **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]
+```bash
+pip install transformers
+```
 
-### Model Sources [optional]
+## Usage
+Here’s a simple example of how to use the model for stance detection in Turkish:
 
-<!-- Provide the basic links for the model. -->
+```bash
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# Load the model and tokenizer
+model_name = "byunal/distilbert-base-turkish-cased-stance"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
-## Uses
+# Example text
+text = "Bu konu hakkında kesinlikle karşıyım."
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+# Tokenize input
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
 
-### Direct Use
+# Perform prediction
+with torch.no_grad():
+    outputs = model(**inputs)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+# Get predicted stance
+predictions = torch.argmax(outputs.logits, dim=-1)
+stance_label = predictions.item()
 
-[More Information Needed]
+# Display result
+labels = ["Favor", "Against", "Neutral"]
+print(f"The stance is: {labels[stance_label]}")
+```
 
-### Downstream Use [optional]
+## Training
+This model was fine-tuned using a specialized Turkish stance detection dataset that uniquely reflects various text contexts and opinions. The dataset includes diverse examples from social media, news articles, and public comments, ensuring a robust understanding of stance detection in real-world applications.
 
-<!-- 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]
+- Epochs: 10
+- Batch Size: 32
+- Learning Rate: 5e-5
+- Optimizer: AdamW
 
 ## 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]
+The model was evaluated using Accuracy and Macro F1-score on a validation dataset. The results confirm the model's effectiveness in stance detection tasks in Turkish.
+- Accuracy Score: % 78.0 
+- Macro F1 Score: % 78.0