Update README.md

README.md

@@ -1,199 +1,178 @@
 ---
 library_name: transformers
-tags: []
+datasets:
+- s-nlp/EverGreen-Multilingual
+language:
+- ru
+- en
+- fr
+- de
+- he
+- ar
+- zh
+base_model:
+- intfloat/multilingual-e5-large-instruct
+pipeline_tag: text-classification
 ---
+# E5-EG-large
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+A lightweight multilingual model for temporal classification of questions, fine-tuned from [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct).
 
 ## 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
+E5-EG-small (E5 EverGreen - Large) is an efficient multilingual text classification model that determines whether questions have temporally mutable or immutable answers. This model offers a balanced trade-off between performance and computational efficiency.
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- **Model type:** Text Classification
+- **Base model:** [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-large-instruct)
+- **Language(s):** Russian, English, French, German, Hebrew, Arabic, Chinese
+- **License:** MIT
 
-[More Information Needed]
+### Model Sources
 
-## Bias, Risks, and Limitations
+- **Repository:** [GitHub](https://github.com/s-nlp/Evergreen-classification)
+- **Paper:** [Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA](https://arxiv.org/abs/2505.21115)
 
-<!-- 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]
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import time
+
+# Load model and tokenizer
+model_name = "s-nlp/E5-EG-small"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# For optimal performance, use GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+model.eval()
+
+# Batch classification example
+questions = [
+    "What is the capital of France?",
+    "Who won the latest World Cup?",
+    "What is the speed of light?",
+    "What is the current Bitcoin price?"
+]
+
+# Tokenize all questions
+inputs = tokenizer(
+    questions, 
+    return_tensors="pt", 
+    padding=True, 
+    truncation=True, 
+    max_length=64
+).to(device)
+
+# Classify
+start_time = time.time()
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_classes = torch.argmax(predictions, dim=-1)
+
+inference_time = (time.time() - start_time) * 1000  # ms
+
+# Display results
+class_names = ["Immutable", "Mutable"]
+for i, question in enumerate(questions):
+    print(f"Q: {question}")
+    print(f"   Classification: {class_names[predicted_classes[i].item()]}")
+    print(f"   Confidence: {predictions[i][predicted_classes[i]].item():.2f}")
+
+print(f"\nTotal inference time: {inference_time:.2f}ms")
+print(f"Average per question: {inference_time/len(questions):.2f}ms")
+```
 
 ## 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]
+Same multilingual dataset as E5-EG-small:
+- ~4,000 questions per language
+- Balanced class distribution
+- Augmented with synthetic and translated data
 
 ### 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]
-
+#### Preprocessing
+- Identical to E5-EG-small
+- Maximum sequence length: 64 tokens
+- Multilingual tokenization
 
 #### Training Hyperparameters
+- **Training regime:** fp16 mixed precision
+- **Epochs:** 10 
+- **Batch size:** 32 
+- **Learning rate:** 5e-05 
+- **Warmup steps:** 300
+- **Weight decay:** 0.01
+- **Optimizer:** AdamW
+- **Loss function:** Focal Loss (γ=2.0, α=0.25) with class weighting
+- **Gradient accumulation steps:** 1
 
-- **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]
+#### Hardware
+- **GPUs:** Single NVIDIA V100
+- **Training time:** ~8 hours
 
 ## 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. -->
+### Testing Data
 
-[More Information Needed]
+Same test sets as E5-EG-large (2100 samples per language).
 
-#### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+### Metrics
 
-[More Information Needed]
+#### Overall Performance
+| Metric | Score |
+|--------|-------|
+| Overall F1 | 0.89 |
+| Overall Accuracy | 0.88 |
 
-#### Metrics
+#### Per-Language F1 Scores
+| Language | F1 Score |
+|----------|----------|
+| English | 0.92 |
+| Chinese | 0.91 |
+| French | 0.90 |
+| German | 0.89 |
+| Russian | 0.88 |
+| Hebrew | 0.87 |
+| Arabic | 0.86 |
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+#### Class-wise Performance
+| Class | Precision | Recall | F1 |
+|-------|-----------|--------|-----|
+| Immutable | 0.87 | 0.90 | 0.88 |
+| Mutable | 0.90 | 0.87 | 0.88 |
 
-[More Information Needed]
+### Model Comparison
 
-### Results
+| Model | Parameters | Overall F1 | Inference Time (ms) |
+|-------|------------|------------|---------------------|
+| E5-EG-large | 560M | 0.89 | 45 |
+| E5-EG-small | 118M | 0.85 | 12 |
+| mDeBERTa-base | 278M | 0.87 | 28 |
+| mBERT | 177M | 0.85 | 20 |
 
-[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. -->
+## Citation
 
 **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]
+```bibtex
+@misc{pletenev2025truetomorrowmultilingualevergreen,
+      title={Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA}, 
+      author={Sergey Pletenev and Maria Marina and Nikolay Ivanov and Daria Galimzianova and Nikita Krayko and Mikhail Salnikov and Vasily Konovalov and Alexander Panchenko and Viktor Moskvoretskii},
+      year={2025},
+      eprint={2505.21115},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.21115}, 
+}
+```