Text Classification
Transformers
ONNX
Safetensors
English
roberta
finance
accounting
spend-classification
procurement
text-embeddings-inference
Instructions to use Saeedhoj/spendcoder-distilroberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Saeedhoj/spendcoder-distilroberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Saeedhoj/spendcoder-distilroberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Saeedhoj/spendcoder-distilroberta") model = AutoModelForSequenceClassification.from_pretrained("Saeedhoj/spendcoder-distilroberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,780 Bytes
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"add_cross_attention": false,
"architectures": [
"RobertaForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"bos_token_id": 0,
"classifier_dropout": null,
"dtype": "float32",
"eos_token_id": 2,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "Engineering & professional services",
"1": "Research & development",
"2": "Facility operations & management",
"3": "Construction & building maintenance",
"4": "Equipment maintenance & repair",
"5": "IT & telecom services",
"6": "Environmental services",
"7": "Utilities, security & housekeeping",
"8": "Other services",
"9": "IT hardware & software",
"10": "Electrical & power equipment",
"11": "Lab instruments & chemicals",
"12": "Vehicles & machinery",
"13": "Other goods"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"is_decoder": false,
"label2id": {
"Construction & building maintenance": 3,
"Electrical & power equipment": 10,
"Engineering & professional services": 0,
"Environmental services": 6,
"Equipment maintenance & repair": 4,
"Facility operations & management": 2,
"IT & telecom services": 5,
"IT hardware & software": 9,
"Lab instruments & chemicals": 11,
"Other goods": 13,
"Other services": 8,
"Research & development": 1,
"Utilities, security & housekeeping": 7,
"Vehicles & machinery": 12
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 12,
"num_hidden_layers": 6,
"pad_token_id": 1,
"tie_word_embeddings": true,
"transformers_version": "5.22.1",
"type_vocab_size": 1,
"use_cache": false,
"vocab_size": 50265
}
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