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Model: diabolic6045/Sanskrit-qwen-7B-Translate-v2 Source: Original Platform
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---
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen2.5-7B-Instruct
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tags:
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- sanskrit
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- translation
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- transliteration
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- qwen
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- axolotl
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- iast
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- devanagari
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- bilingual
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datasets:
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- diabolic6045/Sanskrit-transliteration-chat-dataset
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model-index:
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- name: Sanskrit-qwen-7B-Translate-v2
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results: []
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---
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# Sanskrit-qwen-7B-Translate-v2
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<div align="center">
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<img src="https://huggingface.co/diabolic6045/Sanskrit-qwen-7B-Translate-v2/resolve/main/images/poster.png" alt="Sanskrit AI Poster" width="600" style="margin-bottom: 20px;">
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**A specialized Sanskrit language model for translation and transliteration tasks**
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</div>
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## 🌟 Model Description
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This is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) specifically optimized for Sanskrit language processing. The model has been trained using LoRA (Low-Rank Adaptation) on a comprehensive Sanskrit dataset to excel in three key areas:
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1. **Sanskrit to IAST Transliteration** - Converting Devanagari script to IAST format
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2. **Sanskrit to English Translation** - Translating Sanskrit text to English
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3. **English to Sanskrit Translation** - Translating English text to Sanskrit
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## 🚀 Key Features
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### ✨ **Multi-Modal Sanskrit Processing**
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- **IAST Transliteration**: Accurate conversion from Devanagari to IAST
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- **Bidirectional Translation**: Sanskrit ↔ English translation
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- **Context-Aware**: Preserves meaning and cultural context
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- **Chat-Optimized**: Uses conversation format for natural interactions
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### 🔧 **Technical Improvements Over Previous Model**
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- **Enhanced Base Model**: Upgraded from Qwen2.5-7B-Instruct-1M to Qwen2.5-7B-Instruct
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- **Specialized Dataset**: Trained on `Sanskrit-transliteration-chat-dataset` (vs. previous `Sanskrit-llama`)
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- **Chat Template Format**: Uses structured conversation format for better performance
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- **Optimized LoRA**: Improved LoRA configuration with better target modules
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- **Memory Efficient**: Enhanced with flash attention and gradient checkpointing
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## 📊 Model Specifications
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| Parameter | Value |
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|-----------|-------|
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| **Base Model** | Qwen/Qwen2.5-7B-Instruct |
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| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) |
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| **LoRA Rank** | 16 |
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| **LoRA Alpha** | 32 |
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| **Sequence Length** | 512 tokens |
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| **Training Epochs** | 3 |
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| **Learning Rate** | 2e-05 |
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| **Batch Size** | 2 (micro) × 4 (gradient accumulation) |
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| **Optimizer** | AdamW 8-bit |
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| **Precision** | bfloat16 |
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## 🎯 Intended Uses
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### ✅ **Recommended Use Cases**
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- **Academic Research**: Sanskrit text analysis and translation
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- **Educational Tools**: Learning Sanskrit through translation
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- **Cultural Preservation**: Digitizing Sanskrit manuscripts
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- **Linguistic Studies**: Comparative language analysis
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- **Content Creation**: Sanskrit-English bilingual content
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### ⚠️ **Limitations**
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- **Experimental Model**: Still in development, results may vary
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- **Context Sensitivity**: Performance depends on text complexity
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- **Domain Specific**: Optimized for classical Sanskrit texts
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- **Verification Required**: Important translations should be cross-checked
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## 🛠️ Usage Examples
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### 1. Sanskrit to IAST Transliteration
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "diabolic6045/Sanskrit-qwen-7B-Translate-v2"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Prepare the conversation
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messages = [
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{
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"role": "system",
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"content": "You are a Sanskrit transliteration expert. Convert the given Sanskrit text from Devanagari script to IAST (International Alphabet of Sanskrit Transliteration) format."
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},
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{
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"role": "user",
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"content": "Transliterate this Sanskrit text to IAST: बुद्धिश्चार्थात्परो लोभः सन्तोषः परमं सुखम् ।"
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}
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]
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# Apply chat template and generate
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
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response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
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print(response)
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# Output: buddhiścārthātparo lobhaḥ santoṣaḥ paramaṃ sukham |
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```
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### 2. Sanskrit to English Translation
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```python
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messages = [
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{
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"role": "system",
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"content": "You are a Sanskrit to English translation expert. Translate the given Sanskrit text accurately while preserving the meaning and context."
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},
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{
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"role": "user",
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"content": "Translate this Sanskrit text to English: यद॒ग्नौ सूर्ये॑ वि॒षं पृ॑थि॒व्यामोष॑धीषु॒ यत् ।"
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}
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]
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# Generate translation
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7)
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response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
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print(response)
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# Output: The poison that is in the sun, in the earth and in the herbs...
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```
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### 3. English to Sanskrit Translation
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```python
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messages = [
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{
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"role": "system",
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"content": "You are an English to Sanskrit translation expert. Translate the given English text accurately into Sanskrit while preserving the meaning and context."
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},
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{
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"role": "user",
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"content": "Translate this English text to Sanskrit: May the divine powers protect us and grant us wisdom."
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}
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]
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# Generate Sanskrit translation
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
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response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
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print(response)
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# Output: देवाः अस्मान् रक्षन्तु बुद्धिं च प्रयच्छन्तु ।
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```
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## 🎮 Interactive Demo
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Try the model with our Gradio interface:
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### Run the interactive [demo](https://huggingface.co/spaces/diabolic6045/Sanskrit-qwen-7B-Translate-v2)
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The demo provides:
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- **Mode Selection**: Choose between transliteration and translation modes
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- **Real-time Processing**: Instant results with adjustable parameters
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- **Example Library**: Pre-loaded examples for each mode
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- **Parameter Tuning**: Adjust temperature and max length
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## 📈 Training Details
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### Dataset Information
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- **Source**: `diabolic6045/Sanskrit-transliteration-chat-dataset`
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- **Format**: Chat template with structured conversations
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- **Size**: Comprehensive Sanskrit corpus with multiple translation pairs
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- **Validation Split**: 10% for evaluation
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### Training Configuration
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```yaml
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# Key training parameters
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base_model: Qwen/Qwen2.5-7B-Instruct
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adapter: lora
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lora_r: 16
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lora_alpha: 32
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sequence_len: 512
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num_epochs: 3
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learning_rate: 0.00002
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optimizer: adamw_8bit
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lr_scheduler: cosine
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bf16: auto
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flash_attention: true
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gradient_checkpointing: true
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```
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### Hardware Requirements
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- **Training**: Multi-GPU setup with 24GB+ VRAM per GPU
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- **Inference**: 8GB+ VRAM for optimal performance
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- **CPU**: Compatible with CPU inference (slower)
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## 🔄 Comparison with Previous Model
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| Feature | Previous Model | Current Model |
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|---------|---------------|---------------|
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| **Base Model** | Qwen2.5-7B-Instruct-1M | Qwen2.5-7B-Instruct |
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| **Dataset** | Sanskrit-llama (Alpaca) | Sanskrit-transliteration-chat-dataset |
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| **Format** | Alpaca format | Chat template format |
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| **Capabilities** | Basic translation | Multi-modal (transliteration + translation) |
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| **LoRA Rank** | 32 | 16 (optimized) |
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| **Sequence Length** | 1024 | 512 (focused) |
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| **Training Epochs** | 1 | 3 (more thorough) |
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| **Specialization** | General Sanskrit | Specialized for transliteration |
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## 🛡️ Ethical Considerations
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- **Cultural Sensitivity**: Respect for Sanskrit's cultural and religious significance
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- **Accuracy Disclaimer**: Model outputs should be verified for important translations
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- **Educational Use**: Primarily intended for educational and research purposes
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- **Bias Awareness**: May reflect biases present in training data
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## 📚 Citation
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If you use this model in your research, please cite:
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```bibtex
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@misc{sanskrit-qwen-chat-lora,
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title={Sanskrit-qwen-7B-Translate-v2: A Specialized Sanskrit Translation and Transliteration Model},
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author={Divax Shah (diabolic6045)},
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year={2024},
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url={https://huggingface.co/diabolic6045/Sanskrit-qwen-7B-Translate-v2}
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}
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```
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## 🤝 Contributing
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We welcome contributions to improve this model:
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1. **Dataset Contributions**: High-quality Sanskrit translation pairs
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2. **Evaluation**: Benchmarking and performance analysis
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3. **Bug Reports**: Issues and improvement suggestions
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4. **Documentation**: Usage examples and tutorials
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## 📄 License
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This model is released under the Apache 2.0 License. See the [LICENSE](LICENSE) file for details.
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## 🙏 Acknowledgments
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- **Qwen Team**: For the excellent base model
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- **Axolotl Framework**: For the training infrastructure
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- **Sanskrit Community**: For linguistic guidance and feedback
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- **Open Source Community**: For tools and resources
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---
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<div align="center">
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**Built with ❤️ for Sanskrit language preservation and education**
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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</div>
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# LoRA Fine-tuning Configuration for Sanskrit Translation & Transliteration Enhancement
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base_model: Qwen/Qwen2.5-7B-Instruct
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# Use our custom chat template
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chat_template_jinja: "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a Sanskrit-English bilingual AI assistant created by Divax Shah (diabolic6045). You are specialized in Sanskrit language understanding and translation.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a Sanskrit-English bilingual AI assistant created by Divax Shah (diabolic6045). You are specialized in Sanskrit language understanding and translation.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\\\"name\\\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}"
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datasets:
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- path: diabolic6045/Sanskrit-transliteration-chat-dataset
|
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type: chat_template
|
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field_messages: messages
|
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message_property_mappings:
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role: role
|
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content: content
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roles:
|
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system:
|
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- system
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user:
|
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- user
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assistant:
|
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- assistant
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|
||||
val_set_size: 0.1
|
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output_dir: ./outputs/sanskrit-chat-7b-lora
|
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||||
# LoRA configuration
|
||||
adapter: lora
|
||||
lora_model_dir:
|
||||
|
||||
sequence_len: 512
|
||||
sample_packing: true
|
||||
eval_sample_packing: true
|
||||
|
||||
# LoRA hyperparameters
|
||||
lora_r: 16
|
||||
lora_alpha: 32
|
||||
lora_dropout: 0.05
|
||||
lora_target_modules:
|
||||
- gate_proj
|
||||
- down_proj
|
||||
- up_proj
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||||
- q_proj
|
||||
- v_proj
|
||||
- k_proj
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||||
- o_proj
|
||||
|
||||
gradient_accumulation_steps: 4
|
||||
micro_batch_size: 2
|
||||
num_epochs: 3
|
||||
optimizer: adamw_8bit
|
||||
lr_scheduler: cosine
|
||||
learning_rate: 0.00002
|
||||
|
||||
# Precision configuration
|
||||
bf16: auto
|
||||
tf32: false
|
||||
|
||||
# Memory optimization
|
||||
gradient_checkpointing: true
|
||||
flash_attention: true
|
||||
|
||||
# Training schedule
|
||||
warmup_ratio: 0.1
|
||||
evals_per_epoch: 4
|
||||
saves_per_epoch: 1
|
||||
weight_decay: 0.0
|
||||
logging_steps: 1
|
||||
|
||||
# Wandb configuration
|
||||
wandb_project: sanskrit-qwen
|
||||
wandb_entity:
|
||||
wandb_watch:
|
||||
wandb_name: sanskrit-qwen-7b-lora-chat
|
||||
wandb_log_model:
|
||||
|
||||
# Resume from checkpoint
|
||||
resume_from_checkpoint:
|
||||
|
||||
# Hub model ID (update with your username)
|
||||
# hub_model_id: your-username/Sanskrit-Qwen2.5-7B-LoRA-chat
|
||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are a Sanskrit-English bilingual AI assistant created by Divax Shah (diabolic6045). You are specialized in Sanskrit language understanding and translation.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are a Sanskrit-English bilingual AI assistant created by Divax Shah (diabolic6045). You are specialized in Sanskrit language understanding and translation.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{\"name\": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
57
config.json
Normal file
57
config.json
Normal file
@@ -0,0 +1,57 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 3584,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 18944,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 28,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 4,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.55.2",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 152064
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"repetition_penalty": 1.05,
|
||||
"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "4.55.2"
|
||||
}
|
||||
3
images/poster.png
Normal file
3
images/poster.png
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:7b3a2382e21bd235d0655e3e293cdcfd620f593fd934704cccca0845279c52ff
|
||||
size 1562533
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 4877660776
|
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model-00002-of-00004.safetensors
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3
model-00002-of-00004.safetensors
Normal file
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version https://git-lfs.github.com/spec/v1
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|
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model-00003-of-00004.safetensors
Normal file
3
model-00003-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4330865200
|
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3
model-00004-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1089994880
|
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347
model.safetensors.index.json
Normal file
347
model.safetensors.index.json
Normal file
@@ -0,0 +1,347 @@
|
||||
{
|
||||
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|
||||
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||||
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|
||||
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|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
||||
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
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|
||||
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|
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user