Text Classification
Transformers
TensorBoard
Safetensors
Russian
bert
Trained with AutoTrain
code
text-embeddings-inference
Instructions to use MrAlexGov/BERT-AI-Vacancy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MrAlexGov/BERT-AI-Vacancy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MrAlexGov/BERT-AI-Vacancy")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MrAlexGov/BERT-AI-Vacancy") model = AutoModelForSequenceClassification.from_pretrained("MrAlexGov/BERT-AI-Vacancy") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - autotrain | |
| - text-classification | |
| - code | |
| base_model: google-bert/bert-base-uncased | |
| widget: | |
| - text: Модель для фильрации вакансий на подходящие AI\ML инженеру и прочие | |
| license: mit | |
| language: | |
| - ru | |
| pipeline_tag: text-classification | |
| # Модель для фильрации вакансий на подходящие AI\ML инженеру и прочие | |
| Модель была дообучена на 500 вакансиях для AI\ML инженера и 500 прочих. Для использования фильтрации потока вакансий на подходящие к рассмотрению и не подходящие в различных пайплайнах. | |
| # Model Trained Using AutoTrain | |
| - Problem type: Text Classification | |
| ## Validation Metrics | |
| loss: 0.45051154494285583 | |
| f1: 0.8405797101449275 | |
| precision: 0.8130841121495327 | |
| recall: 0.87 | |
| auc: 0.9207 | |
| accuracy: 0.835 |