Text Classification
Transformers
Safetensors
llama
classification
bias-detection
text-embeddings-inference
Instructions to use suryatmodulus/ReAligned-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suryatmodulus/ReAligned-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="suryatmodulus/ReAligned-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("suryatmodulus/ReAligned-Classifier") model = AutoModelForSequenceClassification.from_pretrained("suryatmodulus/ReAligned-Classifier") - Notebooks
- Google Colab
- Kaggle
File size: 1,036 Bytes
a822037 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | {
"architectures": [
"LlamaForSequenceClassification"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"dtype": "bfloat16",
"eos_token_id": 128001,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": 128001,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"factor": 32.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_theta": 500000.0,
"rope_type": "llama3"
},
"tie_word_embeddings": false,
"transformers_version": "5.2.0",
"use_cache": false,
"vocab_size": 128256,
"num_labels": 2,
"id2label": {
"0": "china_biased",
"1": "western_biased"
},
"label2id": {
"china_biased": 0,
"western_biased": 1
}
} |