Text Classification
Transformers
Safetensors
Russian
English
bert
tiny-bert
rubert-tiny2
binary-classification
jobs
developer-classification
data-analyst-classification
business-analyst-classification
dev-plus-da-plus-ba
r95
v2
Eval Results (legacy)
text-embeddings-inference
Instructions to use AndreiTolmachev/dev_da_roles_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndreiTolmachev/dev_da_roles_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndreiTolmachev/dev_da_roles_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndreiTolmachev/dev_da_roles_1") model = AutoModelForSequenceClassification.from_pretrained("AndreiTolmachev/dev_da_roles_1") - Notebooks
- Google Colab
- Kaggle
| { | |
| "task": "dev_da_vs_other_binary", | |
| "positive_definition": "role_category in TECH_CLASSES AND team_lead==0", | |
| "tech_classes": [ | |
| "Backend", | |
| "Data Analyst", | |
| "Desktop / Systems", | |
| "Embedded", | |
| "Frontend", | |
| "Fullstack", | |
| "ML / AI / Data Scientist", | |
| "Mobile" | |
| ], | |
| "labels": [ | |
| "other", | |
| "tech" | |
| ], | |
| "max_len": 256, | |
| "description_chars": 1200, | |
| "base_model": "cointegrated/rubert-tiny2", | |
| "trained_on": "vacancies_labeled.csv", | |
| "best_epoch": 8, | |
| "best_threshold": 0.37654510140419006, | |
| "best_precision_at_threshold": 0.8796791443850267, | |
| "best_recall_at_threshold": 0.9508670520231214, | |
| "best_roc_auc": 0.9790331422674761, | |
| "target_recall": 0.95, | |
| "pos_weight": 2.481304126337239 | |
| } |