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--- |
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pipeline_tag: text-classification |
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widget: |
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- text: >- |
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NairiSoft is looking for a highly qualified person with deep knowledge and |
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practical experience in Java programming. The selected candidate will be |
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involved in all stages of the development life cycle. |
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example_title: Current Position Requirments 1 |
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- text: >- |
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Ogma Applications is seeking motivated Senior Developers to work on its |
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worldwide projects. The projects are web applications utilizing latest |
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technologies in video webcasting over internet for web browsers, Televisions |
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and telephone systems. In order to succeed in this team, the incumbent must |
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have the passion and energy to work in an entrepreneurial, and fast paced |
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environment. In addition, the Senior Software Engineer must be an |
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experienced senior architect and technical leader with in-depth knowledge of |
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software development processes. As a senior member of the team in Armenia, |
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Senior Software Engineer will be working closely with other developers and |
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peers in the US and other teams around the globe, to analyze, design, |
|
develop, test and deliver the best in class software. |
|
example_title: Current Position Requirments 2 |
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- text: >- |
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Armeconombank OJSC is looking for a .Net Developer to join its team. The |
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Software Developer will take part in design and development projects. |
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example_title: Current Position Requirments 3 |
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language: |
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- en |
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tags: |
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- albert |
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- text-classification |
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- recommendation |
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- job |
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- albert-base-v2 |
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- IT |
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--- |
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This repository contains a Albert model designed for text classification. The architecture of the model is based on the Albert Base v2 model. |
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# Library |
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|
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``` |
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pip install transformers |
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pip install sentencepiece |
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``` |
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# Example |
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```python |
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from transformers import AutoModel,AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained('Apizhai/Albert-IT-JobRecommendation', use_fast=False), |
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model = AutoModel.from_pretrained('Apizhai/Albert-IT-JobRecommendation') |
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``` |
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# Training hyperparameters |
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The following hyperparameters were used during training: |
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- max_seq_length: 128 |
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- max_length: 128 |
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- train_batch_size: 4 |
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- eval_batch_size: 32 |
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- num_train_epochs: 10 |
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- evaluate_during_training: False |
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- evaluate_during_training_steps: 100 |
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- use_multiprocessing: False |
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- fp16: True |
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- save_steps: -1 |
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- save_eval_checkpoints: False |
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- save_model_every_epoch: False |
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- no_cache: True |
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- reprocess_input_data: True |
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- overwrite_output_dir: True |
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- preprocess_inputs: False |
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- num_return_sequences: 1 |
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# Score |
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- f1-score: 0.85574 |
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- macro avg: 0.84748 |
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- weighted avg: 0.81575 |
