| | --- |
| | 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] |
