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--- |
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base_model: jjzha/jobbert-base-cased |
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model-index: |
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- name: jobbert-base-cased-compdecs |
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results: [] |
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license: mit |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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widget: |
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- text: "Would you like to join a major manufacturing company?" |
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--- |
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## 🖊️ Model description |
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This model is a fine-tuned version of [jjzha/jobbert-base-cased](https://huggingface.co/jjzha/jobbert-base-cased). JobBERT is a continuously pre-trained bert-base-cased checkpoint on ~3.2M sentences from job postings. |
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It has been fine tuned with a classification head to binarily classify job advert sentences as being a `company description` or not. |
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The model was trained on **486 manually labelled company description sentences** and **1000 non company description sentences less than 250 characters in length.** |
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It achieves the following results on a held out test set 147 sentences: |
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- Accuracy: 0.92157 |
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| Label | precision | recall | f1-score | support | |
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| ----------- | ----------- | ----------- |----------- |----------- | |
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| not company description | 0.930693 |0.959184|0.944724|98| |
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| company description | 0.913043 |0.857143|0.884211|49| |
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## 🖨️ Use |
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To use the model: |
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``` |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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from transformers import pipeline |
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model = AutoModelForSequenceClassification.from_pretrained("ihk/jobbert-base-cased-compdecs") |
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tokenizer = AutoTokenizer.from_pretrained("ihk/jobbert-base-cased-compdecs") |
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comp_classifier = pipeline('text-classification', model=model, tokenizer=tokenizer) |
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``` |
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An example use is as follows: |
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``` |
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job_sent = "Would you like to join a major manufacturing company?" |
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comp_classifier(job_sent) |
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>> [{'label': 'LABEL_1', 'score': 0.9953641891479492}] |
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``` |
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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)](https://www.gov.uk/government/publications/standard-industrial-classification-of-economic-activities-sic) codes. |
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### ⚖️ Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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### ⚖️ Training results |
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The fine-tuning metrics are as follows: |
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- eval_loss: 0.462236 |
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- eval_runtime: 0.629300 |
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- eval_samples_per_second: 233.582000 |
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- eval_steps_per_second: 15.890000 |
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- epoch: 10.000000 |
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- perplexity: 1.590000 |
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- |
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### ⚖️ Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
