Metric-AI
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armenian-text-embeddings-1

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 ---
 library_name: transformers
+language:
+- en
+- hy
+base_model:
+- intfloat/multilingual-e5-base
 ---
+# Armenian-Text-Embeddings-1
 ## Model Details
+- **Model Name**: Armenian-Text-Embeddings-1
+- **Model Type**: Text Embeddings for Armenian Language
+- **Base Model**: intfloat/multilingual-e5-base
+- **Version**: 1.0.0
+- **License**: Apache 2.0
+- **Last Updated**: November 2024
+- **Model Architecture**: Transformer-based embeddings model
+- **Input**: Armenian text
+- **Output**: Dense vector embeddings
+## Quick Start
+```python
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+tokenizer = AutoTokenizer.from_pretrained('Metric-AI/armenian-text-embeddings-1')
+model = AutoModel.from_pretrained('Metric-AI/armenian-text-embeddings-1')
+def average_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
+    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+# Each input text should start with "query: " or "passage: ", even for non-English texts.
+# For tasks other than retrieval, you can simply use the "query: " prefix.
+input_texts = [
+    'query: Ինչպե՞ս պատրաստել տոլմա',  # How to make tolma
+    'query: Քանի՞ գրամ սպիտակուց է հարկավոր օրական',  # How many grams of protein needed daily
+    """passage: Տոլմայի բաղադրատոմս՝
+    Բաղադրիչներ՝
+    - 500գ աղացած միս
+    - 1 բաժակ բրինձ
+    - Խաղողի տերևներ
+    - 2 գլուխ սոխ
+    - Համեմունքներ՝ աղ, սև պղպեղ, քարի
+    Պատրաստման եղանակը՝
+    1. Միսը խառնել բրնձի, մանր կտրատած սոխի և համեմունքների հետ
+    2. Խաղողի տերևները լվանալ և թողնել տաք ջրի մեջ 10 րոպե
+    3. Լցոնել տերևները և դասավորել կաթսայի մեջ
+    4. Եփել դանդաղ կրակի վրա 45-60 րոպե""",  # Detailed tolma recipe
+    """passage: Սպիտակուցի օրական չափաբաժինը կախված է մարդու քաշից, սեռից և ֆիզիկական ակտիվությունից:
+    Միջին հաշվով, կանանց համար խորհուրդ է տրվում 46-50 գրամ սպիտակուց օրական:
+    Մարզիկների համար այս թիվը կարող է հասնել մինչև 1.6-2 գրամ մարմնի քաշի յուրաքանչյուր կիլոգրամի համար:
+    Հղիների համար պահանջվում է լրացուցիչ 25 գրամ սպիտակուց:
+    Սպիտակուցի հարուստ աղբյուրներ են՝
+    - Հավի միս (31գ/100գ)
+    - Ձու (13գ/100գ)
+    - Ոսպ (25գ/100գ)
+    - Մածուն (3.5գ/100գ)"""] # Detailed protein intake advice
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
+outputs = model(**batch_dict)
+embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+# normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:2] @ embeddings[2:].T) * 100
+print(scores.tolist())
+# [[83.96063232421875, 30.283924102783203], [32.504661560058594, 82.4246826171875]]
+```
+## Intended Use
+### Primary Intended Uses
+- Semantic search in Armenian
+- Document similarity computation
+- Cross-lingual text understanding
+- Text classification tasks
+- Information retrieval
+## Training Data
+### Dataset Details
+- **Source**: Reddit dataset with English-Armenian translations
+- **Size**: 1.08M pairs of rows
+- **Content Type**: Title and body text pairs
+- **Token Statistics**:
+  - Training Set:
+    - Translated Title Tokens: 23,921,393
+    - Translated Body Tokens: 194,200,654
+  - Test Set:
+    - Translated Title Tokens: 242,443
+    - Translated Body Tokens: 1,946,164
+- **Split Ratio**: 99% train, 1% test
+## Training Procedure
+### Training Details
+- **Weight Averaging**:
+  - Base model (multilingual-e5-base): 0.6 weight
+  - Fine-tuned model: 0.4 weight
+- **Training Duration**: 2 days
+- **Hardware**: 4 x NVIDIA A100 40GB GPUs
+- **Training Parameters**:
+  - Epochs: 5
+  - Batch Size: 256 per GPU, (256*4 in total)
+  - Learning Rate: 5e-5
+  - Weight Decay: 0.01
+  - Warmup Steps: 1000
+  - Maximum Sequence Length: 128 tokens
+  - FP16 Training: Enabled
+  - Gradient Clipping: 1.0
+### Optimization Configuration
+- **Framework**: DeepSpeed Stage 2
+- **Optimizer**: AdamW with auto weight decay
+- **Mixed Precision**: FP16 with dynamic loss scaling
+- **ZeRO Optimization**: Stage 2 with:
+  - Allgather partitions
+  - Overlap communications
+  - Contiguous gradients
+- **Additional Features**:
+  - Gradient checkpointing
+  - Tensor parallelism (size: 2)
+## Performance and Limitations
+### Capabilities
+- Effective for semantic similarity tasks in Armenian
+- Suitable for document classification and clustering
+### Limitations
+- Performance may vary on domain-specific terminology
+- May not capture Armenian-specific cultural contexts effectively
+- Limited by the quality of training data translations
+### Known Biases
+- May exhibit biases present in Reddit content
+## Computational Requirements
+### Training Infrastructure
+- 4 x NVIDIA A100 40GB GPUs
+- DeepSpeed-compatible setup
+- Minimum 128GB System RAM
+### Inference Requirements
+- Minimum: 8GB GPU RAM
+- Recommended: 16GB GPU RAM
+- CPU Inference: Possible but significantly slower
+- RAM: 16GB minimum
 ## Environmental Impact
+- **Training Hardware**: 4 x NVIDIA A100 40GB
+- **Training Duration**: 48 hours
+- **Estimated Energy Consumption**: 384 kWh (estimated based on A100 power consumption)
+## Ethical Considerations
+- **Data Privacy**: Training data from public Reddit content
+- **Potential Misuse**: Could be misused for content manipulation or spam
+- **Bias**: May perpetuate social biases present in Reddit content
+- **Recommendations**:
+  - Monitor system outputs for harmful content
+  - Implement content filtering for production use
+  - Regular bias assessment recommended
+## Technical Specifications
+- **Model Size**: ~278M parameters (based on e5-base)
+- **Embedding Dimension**: 384
+- **Max Sequence Length**: 128 tokens
+- **Framework Compatibility**:
+  - PyTorch
+  - Hugging Face Transformers
+  - DeepSpeed
+## Citation
+```bibtex
+@misc{armenian-text-embeddings-2024,
+  author = {[Your Organization]},
+  title = {Armenian-Text-Embeddings-1: Enhanced Armenian Language Embeddings},
+  year = {2024},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{[repository-url]}}
+}
+```
+## Additional Information
+### Base Model References
+- multilingual-e5-base: [https://huggingface.co/intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base)
+### Acknowledgments
+- intfloat for the original multilingual-e5-base model
+- Reddit community for the source content
+- DeepSpeed team for optimization toolkit
+## Version History
+- 1.0.0 (November 2024): Initial release