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onnx-checkpoint (#19)
Browse files- update attrs (22b5104e663691ccd916cf2e641ac19f3f783ff5)
- attrs (53766414e40dcde752946e471e6f1ea3715a5269)
- emb weights (5d54d7368588b4d755617c452941aa06bb178ed6)
- original weights (34952b986b3883fb58019bf5ffbf907fe04080d0)
- lora b (bddf2fe826efc01d3a74443c5c4e8a006f1801f0)
- upload rest off the files (33bf5b537ddbb4b6d85d169794a2c0d58f8a243f)
- Update .gitattributes (936ce791a083ff46807ae9b73a86990d574f58ac)
- update readme (47c6c013fd3ba696007a8de2e2d5ec60384a8866)
- Merge branch 'main' into pr/19 (ddfd663351717b1b7b0b0e5a3732fa3aca1adc7a)
- adjust readme (821cc49d04e5dd8a81f504df8881e76fdb7ca504)
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- .gitattributes +4 -0
- README.md +36 -0
- onnx/_roberta_encoder_layers.0_mixer_rotary_emb_Constant_6_attr__value +3 -0
- onnx/_roberta_encoder_layers.0_mixer_rotary_emb_Constant_attr__value +3 -0
- onnx/model.onnx +3 -0
- onnx/roberta.embeddings.token_type_embeddings.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.embeddings.word_embeddings.parametrizations.weight.0.lora_A +3 -0
- onnx/roberta.embeddings.word_embeddings.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.embeddings.word_embeddings.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.0.mixer.Wqkv.bias +0 -0
- onnx/roberta.encoder.layers.0.mixer.Wqkv.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.0.mixer.Wqkv.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.0.mixer.Wqkv.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.0.mixer.out_proj.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.0.mixer.out_proj.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.0.mixer.out_proj.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.0.mlp.fc1.bias +0 -0
- onnx/roberta.encoder.layers.0.mlp.fc1.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.0.mlp.fc1.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.0.mlp.fc1.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.0.mlp.fc2.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.0.mlp.fc2.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.0.mlp.fc2.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.1.mixer.Wqkv.bias +0 -0
- onnx/roberta.encoder.layers.1.mixer.Wqkv.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.1.mixer.Wqkv.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.1.mixer.Wqkv.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.1.mixer.out_proj.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.1.mixer.out_proj.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.1.mixer.out_proj.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.1.mlp.fc1.bias +0 -0
- onnx/roberta.encoder.layers.1.mlp.fc1.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.1.mlp.fc1.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.1.mlp.fc1.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.1.mlp.fc2.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.1.mlp.fc2.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.1.mlp.fc2.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.10.mixer.Wqkv.bias +0 -0
- onnx/roberta.encoder.layers.10.mixer.Wqkv.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.10.mixer.Wqkv.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.10.mixer.Wqkv.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.10.mixer.out_proj.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.10.mixer.out_proj.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.10.mixer.out_proj.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.10.mlp.fc1.bias +0 -0
- onnx/roberta.encoder.layers.10.mlp.fc1.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.10.mlp.fc1.parametrizations.weight.0.lora_B +0 -0
- onnx/roberta.encoder.layers.10.mlp.fc1.parametrizations.weight.original +3 -0
- onnx/roberta.encoder.layers.10.mlp.fc2.parametrizations.weight.0.lora_A +0 -0
- onnx/roberta.encoder.layers.10.mlp.fc2.parametrizations.weight.0.lora_B +0 -0
.gitattributes
CHANGED
@@ -34,3 +34,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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*.original filter=lfs diff=lfs merge=lfs -text
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onnx/roberta.embeddings.word_embeddings.parametrizations.weight.0.lora_A filter=lfs diff=lfs merge=lfs -text
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onnx/_roberta_encoder_layers.0_mixer_rotary_emb_Constant_6_attr__value filter=lfs diff=lfs merge=lfs -text
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onnx/_roberta_encoder_layers.0_mixer_rotary_emb_Constant_attr__value filter=lfs diff=lfs merge=lfs -text
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README.md
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@@ -21683,6 +21683,42 @@ embeddings = model.encode(
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)
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```
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## Contact
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)
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```
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**<details><summary>ONNX Inference.</summary>**
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<p>
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You can use ONNX for efficient inference with `jina-embeddings-v3`:
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```python
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import onnxruntime
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import numpy as np
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from transformers import AutoTokenizer, PretrainedConfig
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# Load tokenizer and model config
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tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v3')
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config = PretrainedConfig.from_pretrained('jinaai/jina-embeddings-v3')
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# Tokenize input
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input_text = tokenizer('sample text', return_tensors='np')
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# ONNX session
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model_path = 'jina-embeddings-v3/onnx/model.onnx'
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session = onnxruntime.InferenceSession(model_path)
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# Prepare inputs for ONNX model
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task_type = 'text-matching'
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task_id = np.array(config.lora_adaptations.index(task_type), dtype=np.int64)
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inputs = {
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'input_ids': input_text['input_ids'],
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'attention_mask': input_text['attention_mask'],
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'task_id': task_id
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}
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# Run model
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outputs = session.run(None, inputs)
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```
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</p>
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</details>
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## Contact
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onnx/_roberta_encoder_layers.0_mixer_rotary_emb_Constant_6_attr__value
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