Text Classification
Transformers
Safetensors
Russian
English
bert
tiny-bert
rubert-tiny2
binary-classification
jobs
developer-classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use AndreiTolmachev/dev_roles_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndreiTolmachev/dev_roles_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AndreiTolmachev/dev_roles_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AndreiTolmachev/dev_roles_1") model = AutoModelForSequenceClassification.from_pretrained("AndreiTolmachev/dev_roles_1") - Notebooks
- Google Colab
- Kaggle
File size: 729 Bytes
31c5806 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"task": "dev_vs_nondev_binary",
"positive_definition": "role_category in DEV_CLASSES AND team_lead==0",
"dev_classes": [
"Backend",
"Desktop / Systems",
"Embedded",
"Frontend",
"Fullstack",
"ML / AI / Data Scientist",
"Mobile"
],
"labels": [
"non_dev",
"dev"
],
"max_len": 256,
"description_chars": 1200,
"base_model": "cointegrated/rubert-tiny2",
"trained_on": "titles_descriptions_urls_14_05_classified.csv",
"best_epoch": 5,
"best_threshold": 0.6977906227111816,
"best_precision_at_threshold": 0.9682539682539683,
"best_recall_at_threshold": 0.9721115537848606,
"best_roc_auc": 0.9964378937452162,
"target_recall": 0.97,
"pos_weight": 3.871157894736842
} |