Update README.md

README.md

@@ -1,13 +1,23 @@
 ---
 library_name: transformers
-tags: []
+tags:
+- ner
+- token-classification
+- turkish
+license: mit
+language:
+- tr
+metrics:
+- f1
+- accuracy
+- recall
 ---
 
-# Model Card for Model ID
+# Model Card for Turkish Named Entity Recognition Model
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+This model performs Named Entity Recognition (NER) for Turkish text, identifying and classifying entities such as person names, locations, and organizations. Model got 0.9599 F1 on validation set.
 
 ## Model Details
 
@@ -15,185 +25,53 @@ 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 Sources [optional]
+This is a fine-tuned BERT model for Turkish Named Entity Recognition (NER). It is based on the `dbmdz/bert-base-turkish-uncased` model and has been trained on a custom Turkish NER dataset.
 
-<!-- Provide the basic links for the model. -->
+- **Developed by:** Ezel Bayraktar (ai@bayraktarlar.dev)
+- **Model type:** Token Classification (Named Entity Recognition)
+- **Language(s) (NLP):** Turkish
+- **License:** MIT
+- **Finetuned from model:** dbmdz/bert-base-turkish-uncased
 
-- **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 Named Entity Recognition tasks in Turkish text. It identifies and labels entities such as person names (PER), locations (LOC), and organizations (ORG).
 
 ### 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]
+The model can be integrated into larger natural language processing pipelines for Turkish, such as information extraction systems, question answering, or text summarization.
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
-[More Information Needed]
+This model should not be used for languages other than Turkish or for tasks beyond Named Entity Recognition. It may not perform well on domain-specific text or newly emerging named entities not present in the training data.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-[More Information Needed]
+The model may inherit biases present in the training data or the pre-trained BERT model it was fine-tuned from. It may not perform consistently across different domains or types of Turkish text.
 
 ### 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.
+Users should evaluate the model's performance on their specific domain and use case. For critical applications, human review of the model's outputs is recommended.
 
 ## 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
+```python
+from transformers import pipeline
 
-[More Information Needed]
+nert = pipeline('ner', model='TerminatorPower/nerT', tokenizer='TerminatorPower/nerT')
+answer = nert("Mustafa Kemal Atatürk, 19 Mayıs 1919'da Samsun'a çıktı.")
+print(answer)