Text Classification
Transformers
Safetensors
English
bert
spam
ham
email
tinybert
enron
Eval Results (legacy)
text-embeddings-inference
Instructions to use prancyFox/tiny-bert-enron-spam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prancyFox/tiny-bert-enron-spam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prancyFox/tiny-bert-enron-spam")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prancyFox/tiny-bert-enron-spam") model = AutoModelForSequenceClassification.from_pretrained("prancyFox/tiny-bert-enron-spam") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "cell": {}, | |
| "classifier_dropout": null, | |
| "emb_size": 312, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 312, | |
| "id2label": { | |
| "0": "ham", | |
| "1": "spam" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1200, | |
| "label2id": { | |
| "ham": 0, | |
| "spam": 1 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 4, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "pre_trained": "", | |
| "structure": [], | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.55.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |