| --- |
| license: cc-by-4.0 |
| datasets: |
| - bltlab/queryner |
| language: |
| - en |
| metrics: |
| - f1 |
| pipeline_tag: token-classification |
| inference: |
| parameters: |
| aggregation_strategy: "first" |
| --- |
| |
| # 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](https://github.com/bltlab) 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](https://huggingface.co/google-bert/bert-base-uncased). |
|
|
| - **Repository:** [https://github.com/bltlab/query-ner](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](https://github.com/bltlab/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](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] |
