Text Classification
Transformers
PyTorch
Safetensors
Spanish
electra
restaurant
classification
reviews
Instructions to use mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis") model = AutoModelForSequenceClassification.from_pretrained("mrm8488/electricidad-small-finetuned-restaurant-sentiment-analysis") - Notebooks
- Google Colab
- Kaggle
File size: 788 Bytes
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"_name_or_path": "mrm8488/electricidad-small-discriminator",
"architectures": [
"ElectraForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"embedding_size": 128,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 256,
"initializer_range": 0.02,
"intermediate_size": 1024,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "electra",
"num_attention_heads": 4,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"summary_activation": "gelu",
"summary_last_dropout": 0.1,
"summary_type": "first",
"summary_use_proj": true,
"transformers_version": "4.7.0.dev0",
"type_vocab_size": 2,
"vocab_size": 31002
}
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