Update README.md

README.md

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 ---
 library_name: transformers
-tags: []
+tags:
+- sentiment-analysis
+- aspect-based-sentiment-analysis
+- transformers
+- bert
+language:
+- tr
+metrics:
+- accuracy
+base_model:
+- dbmdz/bert-base-turkish-uncased
+pipeline_tag: text-classification
+datasets:
+- Sengil/Turkish-ABSA-Wsynthetic
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Aspect Based Sentiment Analysis with Turkish 🇹🇷 Data
 
+<!-- Provide a quick summary of what the model is/does. -->
+This model performs **Aspect-Based Sentiment Analysis (ABSA) 🚀** for Turkish text. It predicts sentiment polarity (Positive, Neutral, Negative) towards specific aspects within a given sentence.
 
+---
 
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+This model is fine-tuned from the `dbmdz/bert-base-turkish-uncased` pretrained BERT model. It is trained on the **Turkish-ABSA-Wsynthetic** dataset, which contains Turkish restaurant reviews annotated with aspect-based sentiments. The model is capable of identifying the sentiment polarity for specific aspects (e.g., "servis," "fiyatlar") mentioned in Turkish sentences.
 
-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]
+- **Developed by:** Sengil
+- **Language(s):** Turkish 🇹🇷
+- **License:** Apache-2.0
+- **Finetuned from model:** `dbmdz/bert-base-turkish-uncased`
+- **Number of Labels:** 3 (Negative, Neutral, Positive)
 
-### Model Sources [optional]
+### Sources
 
 <!-- Provide the basic links for the model. -->
+- **Notebook:** [ABSA_Turkish_BERT_Based_uncased](https://www.kaggle.com/code/mertsengil/absa-train-w-synthetic-restaurant-reviews)
 
-- **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]
+This model can be used directly for analyzing aspect-specific sentiment in Turkish text, especially in domains like restaurant reviews.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+It can be fine-tuned for similar tasks in different domains (e.g., e-commerce, hotel reviews, or customer feedback analysis).
 
 ### 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. -->
+- Not suitable for tasks unrelated to sentiment analysis or Turkish language.
+- May not perform well on datasets with significantly different domain-specific vocabulary.
 
-[More Information Needed]
+---
 
-### Recommendations
+### Limitations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- May struggle with rare or ambiguous aspects not covered in the training data.
+- May exhibit biases present in the training dataset.
 
-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
+<!-- This section provides code examples and links to further documentation. -->
 
-### Training Data
+```
+!pip install -U transformers
+```
 
-<!-- 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. -->
+Use the code below to get started with the model:
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-### Training Procedure
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Sengil/ABSA-Turkish-bert-based-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("Sengil/ABSA-Turkish-bert-based-uncased")
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+# Example inference
+text = "Servis çok yavaştı ama yemekler lezzetliydi."
+aspect = "servis"
+formatted_text = f"[CLS] {text} [SEP] {aspect} [SEP]"
 
-#### Preprocessing [optional]
+inputs = tokenizer(formatted_text, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
+outputs = model(**inputs)
+predicted_class = outputs.logits.argmax(dim=1).item()
 
-[More Information Needed]
+# Map prediction to label
+labels = {0: "Negative", 1: "Neutral", 2: "Positive"}
+print(f"Sentiment for '{aspect}': {labels[predicted_class]}")
+```
 
 
-#### Training Hyperparameters
+## Training Details
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Training Data
 
-#### Speeds, Sizes, Times [optional]
+Training Data
+The model was fine-tuned on the Turkish-ABSA-Wsynthetic.csv dataset. The dataset contains semi-synthetic Turkish sentences annotated for aspect-based sentiment analysis.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- Training Procedure
+- Optimizer: AdamW
+- Learning Rate: 2e-5
+- Batch Size: 16
+- Epochs: 5
+- Max Sequence Length: 128
 
-[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]
+The model achieved the following scores on the test set:
 
-## More Information [optional]
+- Accuracy: 95.56%
+- F1 Score (Weighted): 95.67%
 
-[More Information Needed]
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+```
+@misc{absa_turkish_bert_based_uncased,
+  title={Aspect-Based Sentiment Analysis for Turkish},
+  author={Sengil},
+  year={2024},
+  url={https://huggingface.co/Sengil/ABSA_Turkish_BERT_Based_uncased}
+}
+```
 
 ## Model Card Contact
 
-[More Information Needed]
+For any questions or issues, please open an issue in the repository or contact [LinkedIN](https://www.linkedin.com/in/mertsengil/).