Text Classification
Transformers
TensorBoard
Safetensors
bert
Generated from Trainer
text-embeddings-inference
Instructions to use Mediocre-Judge/my_mind_classifier_BertMini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mediocre-Judge/my_mind_classifier_BertMini with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mediocre-Judge/my_mind_classifier_BertMini")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mediocre-Judge/my_mind_classifier_BertMini") model = AutoModelForSequenceClassification.from_pretrained("Mediocre-Judge/my_mind_classifier_BertMini", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "prajjwal1/bert-mini", | |
| "architectures": [ | |
| "BertForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "classifier_dropout": null, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 256, | |
| "id2label": { | |
| "0": "category", | |
| "1": "news", | |
| "2": "sports", | |
| "3": "finance", | |
| "4": "video", | |
| "5": "travel", | |
| "6": "foodanddrink", | |
| "7": "lifestyle", | |
| "8": "weather", | |
| "9": "health", | |
| "10": "autos", | |
| "11": "music", | |
| "12": "tv", | |
| "13": "movies", | |
| "14": "entertainment", | |
| "15": "kids", | |
| "16": "middleeast", | |
| "17": "games", | |
| "18": "northamerica" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1024, | |
| "label2id": { | |
| "autos": 10, | |
| "category": 0, | |
| "entertainment": 14, | |
| "finance": 3, | |
| "foodanddrink": 6, | |
| "games": 17, | |
| "health": 9, | |
| "kids": 15, | |
| "lifestyle": 7, | |
| "middleeast": 16, | |
| "movies": 13, | |
| "music": 11, | |
| "news": 1, | |
| "northamerica": 18, | |
| "sports": 2, | |
| "travel": 5, | |
| "tv": 12, | |
| "video": 4, | |
| "weather": 8 | |
| }, | |
| "layer_norm_eps": 1e-12, | |
| "max_position_embeddings": 512, | |
| "model_type": "bert", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 4, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.45.1", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522 | |
| } | |