Update README.md

README.md

@@ -1,235 +1,67 @@
-# Model Card for Model
-
-<!-- Provide a quick summary of what the model is/does. [Optional] -->
-Some cool model...
-
-
-
-
-#  Table of Contents
-
-- [Model Card for Model](#model-card-for--model_id-)
-- [Table of Contents](#table-of-contents)
-- [Table of Contents](#table-of-contents-1)
-- [Model Details](#model-details)
-  - [Model Description](#model-description)
-- [Uses](#uses)
-  - [Direct Use](#direct-use)
-  - [Downstream Use [Optional]](#downstream-use-optional)
-  - [Out-of-Scope Use](#out-of-scope-use)
-- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
-  - [Recommendations](#recommendations)
-- [Training Details](#training-details)
-  - [Training Data](#training-data)
-  - [Training Procedure](#training-procedure)
-    - [Preprocessing](#preprocessing)
-    - [Speeds, Sizes, Times](#speeds-sizes-times)
-- [Evaluation](#evaluation)
-  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
-    - [Testing Data](#testing-data)
-    - [Factors](#factors)
-    - [Metrics](#metrics)
-  - [Results](#results)
-- [Model Examination](#model-examination)
-- [Environmental Impact](#environmental-impact)
-- [Technical Specifications [optional]](#technical-specifications-optional)
-  - [Model Architecture and Objective](#model-architecture-and-objective)
-  - [Compute Infrastructure](#compute-infrastructure)
-    - [Hardware](#hardware)
-    - [Software](#software)
-- [Citation](#citation)
-- [Glossary [optional]](#glossary-optional)
-- [More Information [optional]](#more-information-optional)
-- [Model Card Authors [optional]](#model-card-authors-optional)
-- [Model Card Contact](#model-card-contact)
-- [How to Get Started with the Model](#how-to-get-started-with-the-model)
-
-
-# Model Details
-
-## Model Description
-
-<!-- Provide a longer summary of what this model is/does. -->
-Some cool model...
-
-- **Developed by:** More information needed
-- **Shared by [Optional]:** More information needed
-- **Model type:** Language model
-- **Language(s) (NLP):** en
-- **License:** apache-2.0
-- **Parent Model:** More information needed
-- **Resources for more information:** 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. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info 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 -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
- 
-
-
-
-## Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-
-
-
-
-# Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
-
-
-## Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-
-
-
-
-# Training Details
-
-## Training Data
-
-<!-- This should link to a Data 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 on training data 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
-
-More information needed
-
-### Speeds, Sizes, Times
-
-<!-- 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 Data 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
-
-# Model Examination
-
-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
-
-<!-- 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]
-
-<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
-
-a, s, y
-
-# Model Card Contact
-
-More information needed
-
-# How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-<details>
-<summary> Click to expand </summary>
-
-More information needed
-
-</details>
+language: 
+- tr
+thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
+tags:
+- text-classification
+- emotion
+- pytorch
+datasets:
+- emotion (Translated to Turkish)
+metrics:
+- Accuracy, F1 Score
+---
+# distilbert-base-turkish-cased-emotion
+
+## Model description:
+[Distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) finetuned on the emotion dataset (Translated to Turkish via Google Translate API) using HuggingFace Trainer with below Hyperparameters
+```
+ learning rate 2e-5, 
+ batch size 64,
+ num_train_epochs=8,
+```
+
+## Model Performance Comparision on Emotion Dataset from Twitter:
+
+| Model | Accuracy | F1 Score |  Test Sample per Second |
+| --- | --- | --- | --- |
+| [Distilbert-base-turkish-cased-emotion](https://huggingface.co/zafercavdar/distilbert-base-turkish-cased-emotion) | 83.25 | 83.17 | 232.197 |
+
+## How to Use the model:
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification",
+                       model='zafercavdar/distilbert-base-turkish-cased-emotion',
+                       return_all_scores=True)
+prediction = classifier("Bu kütüphaneyi seviyorum, en iyi yanı kolay kullanımı.", )
+print(prediction)
+
+"""
+Output:
+[
+  [
+    {'label': 'sadness', 'score': 0.0026786490343511105},
+    {'label': 'joy', 'score': 0.6600754261016846},
+    {'label': 'love', 'score': 0.3203163146972656},
+    {'label': 'anger', 'score': 0.004358913749456406},
+    {'label': 'fear', 'score': 0.002354539930820465},
+    {'label': 'surprise', 'score': 0.010216088965535164}
+  ]
+]
+
+"""
+```
+
+## Dataset:
+[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
+
+## Eval results
+```json
+{
+ 'eval_accuracy': 0.8325,
+ 'eval_f1': 0.8317301441160213,
+ 'eval_loss': 0.5021793842315674,
+ 'eval_runtime': 8.6167,
+ 'eval_samples_per_second': 232.108,
+ 'eval_steps_per_second': 3.714
+}
+```