Text Classification
Transformers
Safetensors
English
modernbert
orality
linguistics
rhetorical-analysis
Eval Results (legacy)
text-embeddings-inference
Instructions to use HavelockAI/bert-marker-subtype with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HavelockAI/bert-marker-subtype with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HavelockAI/bert-marker-subtype")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HavelockAI/bert-marker-subtype") model = AutoModelForSequenceClassification.from_pretrained("HavelockAI/bert-marker-subtype", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "ModernBertForSequenceClassification" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 50281, | |
| "classifier_activation": "gelu", | |
| "classifier_bias": false, | |
| "classifier_dropout": 0.0, | |
| "classifier_pooling": "mean", | |
| "cls_token_id": 50281, | |
| "decoder_bias": true, | |
| "deterministic_flash_attn": false, | |
| "dtype": "float32", | |
| "embedding_dropout": 0.0, | |
| "eos_token_id": 50282, | |
| "global_attn_every_n_layers": 3, | |
| "gradient_checkpointing": false, | |
| "hidden_activation": "gelu", | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "abstract_noun", | |
| "1": "additive_formal", | |
| "2": "agent_demoted", | |
| "3": "agentless_passive", | |
| "4": "alliteration", | |
| "5": "anaphora", | |
| "6": "antithesis", | |
| "7": "aside", | |
| "8": "assonance", | |
| "9": "asyndeton", | |
| "10": "audience_response", | |
| "11": "categorical_statement", | |
| "12": "causal_chain", | |
| "13": "causal_explicit", | |
| "14": "citation", | |
| "15": "conceptual_metaphor", | |
| "16": "concessive", | |
| "17": "concessive_connector", | |
| "18": "conditional", | |
| "19": "conflict_frame", | |
| "20": "contrastive", | |
| "21": "cross_reference", | |
| "22": "definitional_move", | |
| "23": "discourse_formula", | |
| "24": "dramatic_pause", | |
| "25": "embodied_action", | |
| "26": "enumeration", | |
| "27": "epistemic_hedge", | |
| "28": "epistrophe", | |
| "29": "epithet", | |
| "30": "everyday_example", | |
| "31": "evidential", | |
| "32": "footnote_reference", | |
| "33": "imperative", | |
| "34": "inclusive_we", | |
| "35": "institutional_subject", | |
| "36": "intensifier_doubling", | |
| "37": "lexical_repetition", | |
| "38": "list_structure", | |
| "39": "metadiscourse", | |
| "40": "methodological_framing", | |
| "41": "named_individual", | |
| "42": "nested_clauses", | |
| "43": "nominalization", | |
| "44": "objectifying_stance", | |
| "45": "parallelism", | |
| "46": "phatic_check", | |
| "47": "phatic_filler", | |
| "48": "polysyndeton", | |
| "49": "probability", | |
| "50": "proverb", | |
| "51": "qualified_assertion", | |
| "52": "refrain", | |
| "53": "relative_chain", | |
| "54": "religious_formula", | |
| "55": "rhetorical_question", | |
| "56": "rhyme", | |
| "57": "rhythm", | |
| "58": "second_person", | |
| "59": "self_correction", | |
| "60": "sensory_detail", | |
| "61": "simple_conjunction", | |
| "62": "specific_place", | |
| "63": "technical_abbreviation", | |
| "64": "technical_term", | |
| "65": "temporal_anchor", | |
| "66": "temporal_embedding", | |
| "67": "third_person_reference", | |
| "68": "tricolon", | |
| "69": "us_them", | |
| "70": "vocative" | |
| }, | |
| "initializer_cutoff_factor": 2.0, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1152, | |
| "label2id": { | |
| "abstract_noun": 0, | |
| "additive_formal": 1, | |
| "agent_demoted": 2, | |
| "agentless_passive": 3, | |
| "alliteration": 4, | |
| "anaphora": 5, | |
| "antithesis": 6, | |
| "aside": 7, | |
| "assonance": 8, | |
| "asyndeton": 9, | |
| "audience_response": 10, | |
| "categorical_statement": 11, | |
| "causal_chain": 12, | |
| "causal_explicit": 13, | |
| "citation": 14, | |
| "conceptual_metaphor": 15, | |
| "concessive": 16, | |
| "concessive_connector": 17, | |
| "conditional": 18, | |
| "conflict_frame": 19, | |
| "contrastive": 20, | |
| "cross_reference": 21, | |
| "definitional_move": 22, | |
| "discourse_formula": 23, | |
| "dramatic_pause": 24, | |
| "embodied_action": 25, | |
| "enumeration": 26, | |
| "epistemic_hedge": 27, | |
| "epistrophe": 28, | |
| "epithet": 29, | |
| "everyday_example": 30, | |
| "evidential": 31, | |
| "footnote_reference": 32, | |
| "imperative": 33, | |
| "inclusive_we": 34, | |
| "institutional_subject": 35, | |
| "intensifier_doubling": 36, | |
| "lexical_repetition": 37, | |
| "list_structure": 38, | |
| "metadiscourse": 39, | |
| "methodological_framing": 40, | |
| "named_individual": 41, | |
| "nested_clauses": 42, | |
| "nominalization": 43, | |
| "objectifying_stance": 44, | |
| "parallelism": 45, | |
| "phatic_check": 46, | |
| "phatic_filler": 47, | |
| "polysyndeton": 48, | |
| "probability": 49, | |
| "proverb": 50, | |
| "qualified_assertion": 51, | |
| "refrain": 52, | |
| "relative_chain": 53, | |
| "religious_formula": 54, | |
| "rhetorical_question": 55, | |
| "rhyme": 56, | |
| "rhythm": 57, | |
| "second_person": 58, | |
| "self_correction": 59, | |
| "sensory_detail": 60, | |
| "simple_conjunction": 61, | |
| "specific_place": 62, | |
| "technical_abbreviation": 63, | |
| "technical_term": 64, | |
| "temporal_anchor": 65, | |
| "temporal_embedding": 66, | |
| "third_person_reference": 67, | |
| "tricolon": 68, | |
| "us_them": 69, | |
| "vocative": 70 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "layer_types": [ | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "local_attention": 128, | |
| "max_position_embeddings": 8192, | |
| "mlp_bias": false, | |
| "mlp_dropout": 0.0, | |
| "model_type": "modernbert", | |
| "norm_bias": false, | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 22, | |
| "pad_token_id": 50283, | |
| "position_embedding_type": "absolute", | |
| "repad_logits_with_grad": false, | |
| "rope_parameters": { | |
| "full_attention": { | |
| "rope_theta": 160000.0, | |
| "rope_type": "default" | |
| }, | |
| "sliding_attention": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "default" | |
| } | |
| }, | |
| "sep_token_id": 50282, | |
| "sparse_pred_ignore_index": -100, | |
| "sparse_prediction": false, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.0.0", | |
| "vocab_size": 50368 | |
| } | |