Text Classification
Transformers
Safetensors
English
deberta-v2
financial-nlp
causal-detection
deberta
sequence-classification
finance
sec-filings
text-embeddings-inference
Instructions to use Imad17700/sec-bert-causal-classifier_s2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Imad17700/sec-bert-causal-classifier_s2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Imad17700/sec-bert-causal-classifier_s2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Imad17700/sec-bert-causal-classifier_s2") model = AutoModelForSequenceClassification.from_pretrained("Imad17700/sec-bert-causal-classifier_s2") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "DebertaV2ForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 1, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "LABEL_0" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "label2id": { | |
| "LABEL_0": 0 | |
| }, | |
| "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": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 1024, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "threshold_F1": 0.35, | |
| "threshold_precision": 0.8227, | |
| "threshold_recall": 0.05, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.8.0", | |
| "type_vocab_size": 0, | |
| "vocab_size": 128100 | |
| } | |