Feature Extraction
Transformers
PyTorch
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use omarelsayeed/Pretrained_Arabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omarelsayeed/Pretrained_Arabert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="omarelsayeed/Pretrained_Arabert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("omarelsayeed/Pretrained_Arabert") model = AutoModel.from_pretrained("omarelsayeed/Pretrained_Arabert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
69080ce
1
Parent(s): 5502289
Upload model
Browse files- config.json +2 -1
- pytorch_model.bin +3 -0
config.json
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{
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"_name_or_path": "/
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"architectures": [
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"BertModel"
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],
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"output_hidden_states": true,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"transformers_version": "4.33.1",
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"type_vocab_size": 2,
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"use_cache": true,
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{
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"_name_or_path": "/root/.cache/torch/sentence_transformers/omarelsayeed_Pretrained_Arabert",
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"architectures": [
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"BertModel"
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],
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"output_hidden_states": true,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"torch_dtype": "float32",
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"transformers_version": "4.33.1",
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"type_vocab_size": 2,
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"use_cache": true,
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:bc6c7db7cba81f24828e6ff3cb15d790372319f295c476d1c174adcdbab2dd98
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size 540837353
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