Sentence Similarity
Transformers
PyTorch
Safetensors
xlm-roberta
feature-extraction
mteb
Eval Results (legacy)
text-embeddings-inference
Instructions to use deepfile/embedder-100p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepfile/embedder-100p with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("deepfile/embedder-100p") model = AutoModel.from_pretrained("deepfile/embedder-100p", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 229 Bytes
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{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.models.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Pooling",
"type": "sentence_transformers.models.Pooling"
}
] |