Instructions to use Tillicollaps/Gender-Classification-russian-name with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tillicollaps/Gender-Classification-russian-name with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tillicollaps/Gender-Classification-russian-name")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tillicollaps/Gender-Classification-russian-name") model = AutoModelForSequenceClassification.from_pretrained("Tillicollaps/Gender-Classification-russian-name") - Notebooks
- Google Colab
- Kaggle
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pipeline_tag: text-classification
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# Model Card for Model ID
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## Model Details
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### Model Description
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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## Uses
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pipeline_tag: text-classification
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### Model Description
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This model is a finely tuned version of padmajabfrl/Gender-Classification. The dataset consists of 26,000 lines.
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"loss": 0.0024
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- **Developed by:** Tillicollaps
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- **Language(s) (NLP):** russian
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## Uses
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