Text Classification
Transformers
Safetensors
Russian
customer-support
hierarchical-classification
mps
minilm
Instructions to use ZenMan67/support-ticket-classifiers-minilm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZenMan67/support-ticket-classifiers-minilm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ZenMan67/support-ticket-classifiers-minilm")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ZenMan67/support-ticket-classifiers-minilm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "task": "auto", | |
| "base_model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", | |
| "architecture": "sentence_pooling_plus_linear", | |
| "pooling": "mean", | |
| "hidden_size": 384, | |
| "num_labels": 18, | |
| "labels": [ | |
| "app_update_instructions", | |
| "clear_app_cache_instructions", | |
| "general_delivery_methods", | |
| "general_loyalty_program_rules", | |
| "general_order_status_meanings", | |
| "general_promo_code_rules", | |
| "general_refund_timeline", | |
| "general_return_policy", | |
| "how_to_contact_support", | |
| "language_settings", | |
| "notification_settings", | |
| "password_reset_instructions", | |
| "pickup_point_search", | |
| "pickup_point_storage_period", | |
| "product_availability_notification", | |
| "subscription_features", | |
| "support_working_hours", | |
| "where_to_find_receipt" | |
| ], | |
| "max_length": 64, | |
| "dropout": 0.2, | |
| "encoder_config": { | |
| "transformers_version": "5.14.1", | |
| "architectures": [ | |
| "BertModel" | |
| ], | |
| "output_hidden_states": false, | |
| "return_dict": true, | |
| "dtype": null, | |
| "chunk_size_feed_forward": 0, | |
| "is_encoder_decoder": false, | |
| "id2label": { | |
| "0": "LABEL_0", | |
| "1": "LABEL_1" | |
| }, | |
| "label2id": { | |
| "LABEL_0": 0, | |
| "LABEL_1": 1 | |
| }, | |
| "problem_type": null, | |
| "vocab_size": 250037, | |
| "hidden_size": 384, | |
| "num_hidden_layers": 12, | |
| "num_attention_heads": 12, | |
| "intermediate_size": 1536, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "attention_probs_dropout_prob": 0.1, | |
| "max_position_embeddings": 512, | |
| "type_vocab_size": 2, | |
| "initializer_range": 0.02, | |
| "layer_norm_eps": 1e-12, | |
| "pad_token_id": 0, | |
| "use_cache": true, | |
| "classifier_dropout": null, | |
| "is_decoder": false, | |
| "add_cross_attention": false, | |
| "bos_token_id": null, | |
| "eos_token_id": null, | |
| "tie_word_embeddings": true, | |
| "_name_or_path": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2", | |
| "gradient_checkpointing": false, | |
| "model_type": "bert", | |
| "position_embedding_type": "absolute", | |
| "output_attentions": false | |
| }, | |
| "checkpoint_dtype": "float16" | |
| } | |