base_model: jjzha/jobbert-base-cased
model-index:
- name: jobbert-base-cased-compdecs
results: []
license: mit
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
widget:
- text: Would you like to join a major manufacturing company?
- text: You must be proficient in Excel.
- text: >-
Meta sells advertising placements for marketers to reach people based on
various factors including age, gender, location, interests, and behavior.
🖊️ Model description
This model is a fine-tuned version of jjzha/jobbert-base-cased. JobBERT is a continuously pre-trained bert-base-cased checkpoint on ~3.2M sentences from job postings.
It has been fine tuned with a classification head to binarily classify job advert sentences as being a company description or not.
The model was trained on 486 manually labelled company description sentences and 1000 non company description sentences less than 250 characters in length.
It achieves the following results on a held out test set 147 sentences:
- Accuracy: 0.92157
| Label | precision | recall | f1-score | support |
|---|---|---|---|---|
| not company description | 0.930693 | 0.959184 | 0.944724 | 98 |
| company description | 0.913043 | 0.857143 | 0.884211 | 49 |
🖨️ Use
To use the model:
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import pipeline
model = AutoModelForSequenceClassification.from_pretrained("ihk/jobbert-base-cased-compdecs")
tokenizer = AutoTokenizer.from_pretrained("ihk/jobbert-base-cased-compdecs")
comp_classifier = pipeline('text-classification', model=model, tokenizer=tokenizer)
An example use is as follows:
job_sent = "Would you like to join a major manufacturing company?"
comp_classifier(job_sent)
>> [{'label': 'LABEL_1', 'score': 0.9953641891479492}]
The intended use of this model is to extract company descriptions from online job adverts to use in downstream tasks such as mapping to Standardised Industrial Classification (SIC) codes.
⚖️ Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
⚖️ Training results
The fine-tuning metrics are as follows: - eval_loss: 0.462236 - eval_runtime: 0.629300 - eval_samples_per_second: 233.582000 - eval_steps_per_second: 15.890000 - epoch: 10.000000 - perplexity: 1.590000
⚖️ Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3