Text Classification
Transformers
Safetensors
multilingual
distilbert
phishing
email-security
text-embeddings-inference
Instructions to use eugenioderodev/fishstop-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eugenioderodev/fishstop-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eugenioderodev/fishstop-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eugenioderodev/fishstop-bert") model = AutoModelForSequenceClassification.from_pretrained("eugenioderodev/fishstop-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,104 Bytes
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"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"bos_token_id": null,
"dim": 768,
"dropout": 0.1,
"dtype": "float32",
"eos_token_id": null,
"fishstop_chunk_aggregation": "maximum_positive_logit_margin",
"fishstop_dataset_sha256": "c89e1f5d2bb92b5a5dd5beb8a235a798932ed19eb9d75c2f740a94b5fd450816",
"fishstop_positive_label_id": 1,
"fishstop_preprocessing": "src.bert_input.normalize_bert_text",
"fishstop_split_strategy": "campaign_grouped_random_stratified_70_10_20",
"hidden_dim": 3072,
"id2label": {
"0": "LEGITIMATE",
"1": "MALICIOUS"
},
"initializer_range": 0.02,
"label2id": {
"LEGITIMATE": 0,
"MALICIOUS": 1
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"output_past": true,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"tie_word_embeddings": true,
"transformers_version": "5.13.1",
"use_cache": false,
"vocab_size": 119547
}
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