Text Classification
Transformers
PyTorch
ONNX
English
albert
text-classfication
int8
Intel® Neural Compressor
neural-compressor
PostTrainingStatic
Instructions to use INC4AI/albert-base-v2-sst2-int8-static-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use INC4AI/albert-base-v2-sst2-int8-static-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="INC4AI/albert-base-v2-sst2-int8-static-inc")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("INC4AI/albert-base-v2-sst2-int8-static-inc") model = AutoModelForSequenceClassification.from_pretrained("INC4AI/albert-base-v2-sst2-int8-static-inc") - Notebooks
- Google Colab
- Kaggle
File size: 1,051 Bytes
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"_name_or_path": "Alireza1044/albert-base-v2-sst2",
"architectures": [
"AlbertForSequenceClassification"
],
"attention_probs_dropout_prob": 0,
"bos_token_id": 2,
"classifier_dropout_prob": 0.1,
"down_scale_factor": 1,
"embedding_size": 128,
"eos_token_id": 3,
"finetuning_task": "sst2",
"gap_size": 0,
"hidden_act": "gelu_new",
"hidden_dropout_prob": 0,
"hidden_size": 768,
"initializer_range": 0.02,
"inner_group_num": 1,
"intermediate_size": 3072,
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "albert",
"net_structure_type": 0,
"num_attention_heads": 12,
"num_hidden_groups": 1,
"num_hidden_layers": 12,
"num_memory_blocks": 0,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"problem_type": "single_label_classification",
"torch_dtype": "int8",
"transformers_version": "4.18.0",
"type_vocab_size": 2,
"vocab_size": 30000,
"label2id": {
"negative": 0,
"positive": 1
},
"id2label": {
"0": "negative",
"1": "positive"
}
} |