Text Classification
Transformers
Safetensors
Russian
bert
multilabel-classification
requirements-engineering
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use eternalGenius/rubert_level1_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eternalGenius/rubert_level1_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="eternalGenius/rubert_level1_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("eternalGenius/rubert_level1_v2") model = AutoModelForSequenceClassification.from_pretrained("eternalGenius/rubert_level1_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Manual update threshold of class Other
Browse files- thresholds.json +1 -1
thresholds.json
CHANGED
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@@ -1,7 +1,7 @@
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{
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"IsFunctional": 0.4,
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"IsBusiness": 0.45,
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-
"Other (OT)": 0.
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"Availability (A)": 0.4,
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"Fault Tolerance (FT)": 0.35,
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| 7 |
"Legal (L)": 0.15,
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{
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"IsFunctional": 0.4,
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| 3 |
"IsBusiness": 0.45,
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+
"Other (OT)": 0.4,
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| 5 |
"Availability (A)": 0.4,
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| 6 |
"Fault Tolerance (FT)": 0.35,
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| 7 |
"Legal (L)": 0.15,
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