Model Card for Model ID
E-commerce query segmentation model in English. This model is trained on QueryNER training dataset with the addition of augmentations so the model should be more robust to spelling mistakes and mentions unseen in the training data.
Model Details
Model Description
This is a token classification model using BERT base uncased as the base model. The model is fine-tuned on the (QueryNER training dataset)[https://huggingface.co/datasets/bltlab/queryner] and augmented data as described in the QueryNER paper.
- Developed by: BLT Lab in collaboration with eBay.
- Funded by: eBay
- Shared by: (@cpalenmichel)[https://github.com/cpalenmichel]
- Model type: Token Classification / Sequence Labeling / Chunking
- Language(s) (NLP): English
- License: CC-BY 4.0
- Finetuned from model: BERT base uncased
Model Sources
Underlying model is based on BERT base-uncased.
- Repository: https://github.com/bltlab/query-ner
- Paper: Accepted at LREC-COLING Coming soon
Uses
Direct Use
Intended use is research purposes and e-commerce query segmentation.
Downstream Use
Potential downstream use cases include weighting entity spans, linking to knowledge bases, removing spans as a recovery strategy for null and low recall queries.
Out-of-Scope Use
This model is trained only on the training data of the QueryNER dataset. It may not perform well on other domains without additional training data and further fine-tuning.
Bias, Risks, and Limitations
See paper limitations section.
How to Get Started with the Model
See huggingface tutorials for token classification and access the model using AutoModelForTokenClassification. Note that we do some post processing to make use of only the first subtoken's tag unlike the inference API.
Training Details
Training Data
See paper for details.
Training Procedure
See paper for details.
Training Hyperparameters
See paper for details.
Evaluation
Evaluation details provided in the paper. Scoring was done using SeqScore using the conlleval repair method for invalid label transition sequences.
Testing Data, Factors & Metrics
Testing Data
QueryNER test set: https://huggingface.co/datasets/bltlab/queryner
Factors
Evaluation is reported with micro-F1 at the entity level on the QueryNER test set. We used conlleval repair method for invalid label transitions.
Metrics
We use micro-F1 at the entity level as this is fairly common practice for NER models.
Results
[More Information Needed]
Environmental Impact
Rough estimate
- Hardware Type: 1 RTX 3090 GPU
- Hours used: < 2 hours
- Cloud Provider: Private
- Compute Region: northamerica-northeast1
- Carbon Emitted: 0.02
Citation
Accepted at LREC-COLING coming soon
BibTeX:
Accepted at LREC-COLING coming soon
Model Card Authors
Chester Palen-Michel (@cpalenmichel)[https://github.com/cpalenmichel]
Model Card Contact
Chester Palen-Michel (@cpalenmichel)[https://github.com/cpalenmichel]