uvegesistvan
/

proposal_2b_german_to_hungarian_PT_label_v2

@@ -1,199 +1,77 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- 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]
-<!-- Provide the basic links for the model. -->
-- **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]
-### 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. -->
-[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]
-## 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]

 ---
+language:
+  - de
+  - hu
+license: mit
+tags:
+  - text-classification
+  - emotion-detection
+  - machine-learning
+  - german
+  - hungarian
+model_name: uvegesistvan/proposal_2b_german_to_hungarian_PT_label_v2
+datasets: []
+metrics:
+  - precision
+  - recall
+  - f1-score
+  - accuracy
 ---
+# Proposal 2B: German to Hungarian Emotion Labeling (v2)
+## Model Description
+This model is designed for **emotion classification** in Hungarian texts.
+It was fine-tuned to recognize **nine emotion categories** and trained on a dataset with labeled examples.
+## Labels and Their Meanings
+| Label | Emotion      |
+|--------|----------------|
+| 0      | Anger        |
+| 1      | Fear         |
+| 2      | Disgust      |
+| 3      | Sadness      |
+| 4      | Joy          |
+| 5      | None of them |
+| 6      | Enthusiasm   |
+| 7      | Hope         |
+| 8      | Pride        |
+## Evaluation Metrics
+The model was evaluated using **precision, recall, f1-score, and accuracy**.
+### Classification Report
+| Label | Precision | Recall | F1-score | Support |
+|--------|------------|--------|----------|---------|
+| Anger (0)        | 0.53 | 0.57 | 0.55 | 777 |
+| Fear (1)         | 0.89 | 0.73 | 0.80 | 776 |
+| Disgust (2)      | 0.92 | 0.95 | 0.93 | 776 |
+| Sadness (3)      | 0.86 | 0.85 | 0.86 | 775 |
+| Joy (4)          | 0.84 | 0.80 | 0.82 | 736 |
+| None of them (5) | 0.65 | 0.66 | 0.66 | 1594 |
+| Enthusiasm (6)   | 0.62 | 0.64 | 0.63 | 776 |
+| Hope (7)         | 0.52 | 0.52 | 0.52 | 777 |
+| Pride (8)        | 0.76 | 0.79 | 0.77 | 776 |
+#### Overall Performance:
+- **Accuracy**: 72%
+- **Macro Avg**: Precision: 0.73, Recall: 0.72, F1-score: 0.73
+- **Weighted Avg**: Precision: 0.72, Recall: 0.72, F1-score: 0.72
+## How to Use
+To use this model for text classification in Python:
+```python
+from transformers import pipeline
+classifier = pipeline("text-classification", model="uvegesistvan/proposal_2b_german_to_hungarian_PT_label_v2")
+text = "Ich bin sehr glücklich, dass du hier bist!"  # German
+result = classifier(text)
+print(result)