Text Classification
Transformers
Safetensors
English
deberta-v2
education
mathematics
misconception-detection
evaluation
text-embeddings-inference
Instructions to use QuantumLearningMachines/qlm-map-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantumLearningMachines/qlm-map-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="QuantumLearningMachines/qlm-map-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("QuantumLearningMachines/qlm-map-classifier") model = AutoModelForSequenceClassification.from_pretrained("QuantumLearningMachines/qlm-map-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "microsoft/deberta-v3-base", | |
| "architectures": [ | |
| "DebertaV2ForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "Incomplete", | |
| "1": "Additive", | |
| "2": "Duplication", | |
| "3": "Wrong_Fraction", | |
| "4": "Subtraction", | |
| "5": "Positive", | |
| "6": "Wrong_term", | |
| "7": "Irrelevant", | |
| "8": "Inversion", | |
| "9": "Mult", | |
| "10": "Denominator-only_change", | |
| "11": "Whole_numbers_larger", | |
| "12": "Adding_across", | |
| "13": "WNB", | |
| "14": "Tacking", | |
| "15": "Unknowable", | |
| "16": "SwapDividend", | |
| "17": "Scale", | |
| "18": "Not_variable", | |
| "19": "Firstterm", | |
| "20": "Adding_terms", | |
| "21": "Multiplying_by_4", | |
| "22": "FlipChange", | |
| "23": "Division", | |
| "24": "Definition", | |
| "25": "Interior", | |
| "26": "Longer_is_bigger", | |
| "27": "Ignores_zeroes", | |
| "28": "Shorter_is_bigger", | |
| "29": "Base_rate", | |
| "30": "Inverse_operation", | |
| "31": "Certainty", | |
| "32": "Incorrect_equivalent_fraction_addition", | |
| "33": "Wrong_Operation" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "Adding_across": 12, | |
| "Adding_terms": 20, | |
| "Additive": 1, | |
| "Base_rate": 29, | |
| "Certainty": 31, | |
| "Definition": 24, | |
| "Denominator-only_change": 10, | |
| "Division": 23, | |
| "Duplication": 2, | |
| "Firstterm": 19, | |
| "FlipChange": 22, | |
| "Ignores_zeroes": 27, | |
| "Incomplete": 0, | |
| "Incorrect_equivalent_fraction_addition": 32, | |
| "Interior": 25, | |
| "Inverse_operation": 30, | |
| "Inversion": 8, | |
| "Irrelevant": 7, | |
| "Longer_is_bigger": 26, | |
| "Mult": 9, | |
| "Multiplying_by_4": 21, | |
| "Not_variable": 18, | |
| "Positive": 5, | |
| "Scale": 17, | |
| "Shorter_is_bigger": 28, | |
| "Subtraction": 4, | |
| "SwapDividend": 16, | |
| "Tacking": 14, | |
| "Unknowable": 15, | |
| "WNB": 13, | |
| "Whole_numbers_larger": 11, | |
| "Wrong_Fraction": 3, | |
| "Wrong_Operation": 33, | |
| "Wrong_term": 6 | |
| }, | |
| "layer_norm_eps": 1e-07, | |
| "legacy": true, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta-v2", | |
| "norm_rel_ebd": "layer_norm", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 768, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.48.0", | |
| "type_vocab_size": 0, | |
| "vocab_size": 128100 | |
| } | |