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
File size: 814 Bytes
69eb4b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"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
} |