Text Classification
Transformers
Safetensors
modernbert
feature-extraction
agentic-intelligence-lab
elephant
rerank
reranker
cross-encoder
text-ranking
retrieval
rag
agents
routing
matryoshka
2d-matryoshka
long-context
Eval Results (legacy)
text-embeddings-inference
Instructions to use agentic-in/elephant-rerank-v1-text-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agentic-in/elephant-rerank-v1-text-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="agentic-in/elephant-rerank-v1-text-small")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("agentic-in/elephant-rerank-v1-text-small") model = AutoModel.from_pretrained("agentic-in/elephant-rerank-v1-text-small") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_name": "llm-semantic-router/mmbert-32k-yarn", | |
| "output_dir": "/workspace/outputs/mmbert-rerank-32k-2d-matryoshka", | |
| "use_2d_matryoshka": true, | |
| "layer_indices": "3,6,11,22", | |
| "dim_indices": "768,512,256,128,64", | |
| "pooling_strategy": "cls", | |
| "train_data": "/workspace/data/bge-m3/bge-m3-data", | |
| "max_length": 32768, | |
| "max_samples": null, | |
| "negatives_per_query": 3, | |
| "use_quora": false, | |
| "use_fever": false, | |
| "max_quora_samples": 100000, | |
| "max_fever_samples": 100000, | |
| "epochs": 1, | |
| "batch_size": 16, | |
| "gradient_accumulation_steps": 2, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.01, | |
| "warmup_ratio": 0.1, | |
| "max_grad_norm": 1.0, | |
| "use_flash_attn": true, | |
| "bf16": true, | |
| "gradient_checkpointing": true, | |
| "num_workers": 4, | |
| "logging_steps": 100, | |
| "save_steps": 5000, | |
| "seed": 42 | |
| } |