Text Classification
Transformers
PyTorch
Safetensors
Serbian
electra
serbian
sentiment-analysis
wordnet
sentiwordnet
lexicon-induction
Instructions to use Tanor/BERTicSENTNEG4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tanor/BERTicSENTNEG4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tanor/BERTicSENTNEG4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tanor/BERTicSENTNEG4") model = AutoModelForSequenceClassification.from_pretrained("Tanor/BERTicSENTNEG4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload ElectraForSequenceClassification
Browse files- config.json +38 -0
- pytorch_model.bin +3 -0
config.json
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{
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"_name_or_path": "classla/bcms-bertic",
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"architectures": [
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"ElectraForSequenceClassification"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"embedding_size": 768,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"id2label": {
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"0": "NON-NEGATIVE",
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"1": "NEGATIVE"
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},
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"label2id": {
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"NEGATIVE": 1,
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"NON-NEGATIVE": 0
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},
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "electra",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"summary_activation": "gelu",
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"summary_last_dropout": 0.1,
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"summary_type": "first",
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"summary_use_proj": true,
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"torch_dtype": "float32",
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"transformers_version": "4.30.2",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 32000
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:6b3740b08476eb30ac641eaf010f90e2a14ebe6f517bdf0517e4d18926e5959d
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size 442546101
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