Token Classification
Transformers
Safetensors
Persian
bert
feature-extraction
persian
word-importance
salience
dhh
asr-evaluation
ace-metric
distillation
Instructions to use Reza2kn/ShenavaSanj-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Reza2kn/ShenavaSanj-v1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Reza2kn/ShenavaSanj-v1.0")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Reza2kn/ShenavaSanj-v1.0") model = AutoModel.from_pretrained("Reza2kn/ShenavaSanj-v1.0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metrics after epoch 2
Browse files- metrics.json +6 -0
metrics.json
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"val_token_spearman": 0.9282,
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"val_per_utt_spearman": 0.9089,
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"val_mse": 0.01239
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}
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],
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"config": {
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"val_token_spearman": 0.9282,
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"val_per_utt_spearman": 0.9089,
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"val_mse": 0.01239
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},
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{
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"epoch": 2,
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"val_token_spearman": 0.9318,
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"val_per_utt_spearman": 0.9122,
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"val_mse": 0.01189
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}
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],
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"config": {
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