Text Classification
Transformers
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use Fryktlos/lets_try with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fryktlos/lets_try with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fryktlos/lets_try")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fryktlos/lets_try") model = AutoModelForSequenceClassification.from_pretrained("Fryktlos/lets_try", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 784 Bytes
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"_name_or_path": "savasy/bert-base-turkish-sentiment-cased",
"architectures": [
"BertForSequenceClassification"
],
"attention_probs_dropout_prob": 0.1,
"classifier_dropout": null,
"finetuning_task": "sst-2",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"id2label": {
"0": "negatif",
"1": "pozitif"
},
"initializer_range": 0.02,
"intermediate_size": 3072,
"label2id": {
"negatif": 0,
"pozitif": 1
},
"layer_norm_eps": 1e-12,
"max_position_embeddings": 512,
"model_type": "bert",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 0,
"position_embedding_type": "absolute",
"transformers_version": "4.35.2",
"type_vocab_size": 2,
"use_cache": true,
"vocab_size": 32000
}
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