Instructions to use divilian/polarops with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use divilian/polarops with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="divilian/polarops")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("divilian/polarops") model = AutoModelForSequenceClassification.from_pretrained("divilian/polarops") - Notebooks
- Google Colab
- Kaggle
Upload DistilBertForSequenceClassification
Browse files- README.md +3 -3
- config.json +24 -0
- model.safetensors +3 -0
README.md
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---
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pipeline_tag: text-classification
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tags:
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---
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---
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pipeline_tag: text-classification
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tags:
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- transformers
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- text-classification
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- polarops
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---
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config.json
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{
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"activation": "gelu",
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"architectures": [
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"DistilBertForSequenceClassification"
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],
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"attention_dropout": 0.1,
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"dim": 768,
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"dropout": 0.1,
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"hidden_dim": 3072,
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"initializer_range": 0.02,
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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"n_heads": 12,
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"n_layers": 6,
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"pad_token_id": 0,
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"problem_type": "single_label_classification",
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"qa_dropout": 0.1,
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"tie_weights_": true,
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"torch_dtype": "float32",
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"transformers_version": "4.52.4",
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"vocab_size": 30522
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9eabb6e3c5db76f4ea283a6d9fc8d6464846e59e203040bf896a16a63ce82ca4
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size 267832560
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