Instructions to use Fizzarolli/thingy-classifier-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fizzarolli/thingy-classifier-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fizzarolli/thingy-classifier-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fizzarolli/thingy-classifier-v1") model = AutoModelForSequenceClassification.from_pretrained("Fizzarolli/thingy-classifier-v1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "kiddothe2b/longformer-mini-1024", | |
| "architectures": ["LongformerForSequenceClassification"], | |
| "attention_mode": "longformer", | |
| "attention_probs_dropout_prob": 0.1, | |
| "attention_window": [128, 128, 128, 128, 128, 128], | |
| "bos_token_id": null, | |
| "classifier_dropout": null, | |
| "cls_token_id": 101, | |
| "eos_token_id": null, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 256, | |
| "ignore_attention_mask": false, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1024, | |
| "layer_norm_eps": 1e-5, | |
| "max_position_embeddings": 1026, | |
| "max_sentence_length": 128, | |
| "max_sentence_size": 128, | |
| "max_sentences": 8, | |
| "model_max_length": 1024, | |
| "model_type": "longformer", | |
| "num_attention_heads": 4, | |
| "num_hidden_layers": 6, | |
| "pad_token_id": 0, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "sep_token_id": 102, | |
| "seq_length": 1024, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.18.0", | |
| "type_vocab_size": 2, | |
| "use_cache": true, | |
| "vocab_size": 30522, | |
| "id2label": { | |
| "0": "EDUCATION", | |
| "1": "STORY", | |
| "2": "SPAM" | |
| }, | |
| "label2id": { | |
| "EDUCATION": 0, | |
| "STORY": 1, | |
| "SPAM": 2 | |
| } | |
| } | |