Text Classification
Transformers
Safetensors
English
roberta
policy
coherence
government
text-embeddings-inference
Instructions to use akaburia/policy-evaluations with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akaburia/policy-evaluations with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="akaburia/policy-evaluations")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("akaburia/policy-evaluations") model = AutoModelForSequenceClassification.from_pretrained("akaburia/policy-evaluations", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 877 Bytes
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"_num_labels": 3,
"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": 1024,
"id2label": {
"0": "neutral",
"1": "coherent",
"2": "incoherent"
},
"initializer_range": 0.02,
"intermediate_size": 4096,
"label2id": {
"coherent": 1,
"incoherent": 2,
"neutral": 0
},
"layer_norm_eps": 1e-05,
"max_position_embeddings": 514,
"model_type": "roberta",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"pad_token_id": 1,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"transformers_version": "4.57.1",
"type_vocab_size": 1,
"use_cache": true,
"vocab_size": 50265
}
|