Feature Extraction
sentence-transformers
ONNX
Safetensors
Transformers
Russian
English
t5
mteb
Eval Results (legacy)
Instructions to use Gotoro/FRIDA-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Gotoro/FRIDA-onnx with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Gotoro/FRIDA-onnx") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use Gotoro/FRIDA-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Gotoro/FRIDA-onnx")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Gotoro/FRIDA-onnx") model = AutoModel.from_pretrained("Gotoro/FRIDA-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload 3 files
Browse files- .gitattributes +1 -0
- config.json +5 -5
- model.onnx +3 -0
- model.onnx_data +3 -0
.gitattributes
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config.json
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"_name_or_path": "FRIDA",
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"architectures": [
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"T5EncoderModel"
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"decoder_start_token_id": 0,
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"dense_act_fn": "gelu_new",
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"dropout_rate": 0.1,
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"gradient_checkpointing": false,
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"is_encoder_decoder":
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"relative_attention_max_distance": 128,
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"tie_word_embeddings": false,
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"use_cache": true,
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"architectures": [
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"decoder_start_token_id": 0,
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"dense_act_fn": "gelu_new",
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"dropout_rate": 0.1,
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"dtype": "float32",
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"export_model_type": "transformer",
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"feed_forward_proj": "gated-gelu",
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"gradient_checkpointing": false,
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"initializer_factor": 1.0,
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"is_encoder_decoder": false,
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"is_gated_act": true,
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"layer_norm_epsilon": 1e-06,
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"model_type": "t5",
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"relative_attention_max_distance": 128,
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"relative_attention_num_buckets": 32,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.6",
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"use_cache": false,
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"vocab_size": 93651
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}
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model.onnx
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size 577205
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model.onnx_data
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