Text Classification
Transformers
Safetensors
English
marks
feature-extraction
finance
earnings-calls
multi-task
regression
sec
quantitative-finance
custom_code
Instructions to use BinomialTechnologies/binomial-marks-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BinomialTechnologies/binomial-marks-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BinomialTechnologies/binomial-marks-1", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BinomialTechnologies/binomial-marks-1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "marks", | |
| "architectures": [ | |
| "MarksMultiHead" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_marks.MarksConfig", | |
| "AutoModel": "modeling_marks.MarksMultiHead" | |
| }, | |
| "encoder_name_or_path": "answerdotai/ModernBERT-large", | |
| "encoder_config": { | |
| "transformers_version": "5.6.2", | |
| "architectures": [ | |
| "ModernBertForMaskedLM" | |
| ], | |
| "output_hidden_states": false, | |
| "return_dict": true, | |
| "dtype": "float32", | |
| "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": 50368, | |
| "hidden_size": 1024, | |
| "intermediate_size": 2624, | |
| "num_hidden_layers": 28, | |
| "num_attention_heads": 16, | |
| "hidden_activation": "gelu", | |
| "max_position_embeddings": 8192, | |
| "initializer_range": 0.02, | |
| "initializer_cutoff_factor": 2.0, | |
| "norm_eps": 1e-05, | |
| "norm_bias": false, | |
| "pad_token_id": 50283, | |
| "eos_token_id": 50282, | |
| "bos_token_id": 50281, | |
| "cls_token_id": 50281, | |
| "sep_token_id": 50282, | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "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", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention", | |
| "sliding_attention", | |
| "sliding_attention", | |
| "full_attention" | |
| ], | |
| "rope_parameters": { | |
| "sliding_attention": { | |
| "rope_type": "default", | |
| "rope_theta": 10000.0 | |
| }, | |
| "full_attention": { | |
| "rope_type": "default", | |
| "rope_theta": 160000.0 | |
| } | |
| }, | |
| "local_attention": 128, | |
| "embedding_dropout": 0.0, | |
| "mlp_bias": false, | |
| "mlp_dropout": 0.0, | |
| "decoder_bias": true, | |
| "classifier_pooling": "mean", | |
| "classifier_dropout": 0.0, | |
| "classifier_bias": false, | |
| "classifier_activation": "gelu", | |
| "deterministic_flash_attn": false, | |
| "sparse_prediction": false, | |
| "sparse_pred_ignore_index": -100, | |
| "tie_word_embeddings": true, | |
| "_name_or_path": "answerdotai/ModernBERT-large", | |
| "global_attn_every_n_layers": 3, | |
| "gradient_checkpointing": false, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "modernbert", | |
| "position_embedding_type": "absolute", | |
| "output_attentions": false | |
| }, | |
| "max_position_embeddings": 16384, | |
| "marks_rope_strategy": "yarn", | |
| "original_max_position": 8192, | |
| "head_dim_ratio": 4, | |
| "dropout": 0.1, | |
| "topic_score_range": [ | |
| -2.0, | |
| 2.0 | |
| ], | |
| "tone_score_range": [ | |
| 1.0, | |
| 5.0 | |
| ], | |
| "topics": [ | |
| "guidance", | |
| "revenue_growth", | |
| "margins", | |
| "demand", | |
| "buybacks", | |
| "dividends", | |
| "m_and_a", | |
| "headcount", | |
| "macro_exposure", | |
| "competition" | |
| ], | |
| "tones": [ | |
| "mgmt_confidence", | |
| "mgmt_defensiveness", | |
| "analyst_skepticism" | |
| ], | |
| "loss_weights": { | |
| "topic_mentioned": 0.5, | |
| "topic_score": 1.0, | |
| "tone_scores": 0.5 | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "5.6.2" | |
| } |