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+ ---
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+ license: cc-by-4.0
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+ datasets:
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+ - bltlab/queryner
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+ language:
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+ - en
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+ metrics:
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+ - f1
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+ pipeline_tag: token-classification
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ E-commerce query segmentation model in English.
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+ 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.
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ This is a token classification model using BERT base uncased as the base model.
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+ 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.
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+
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+
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+ - **Developed by:** [BLT Lab](https://github.com/bltlab) in collaboration with eBay.
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+ - **Funded by:** eBay
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+ - **Shared by:** (@cpalenmichel)[https://github.com/cpalenmichel]
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+ - **Model type:** Token Classification / Sequence Labeling / Chunking
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+ - **Language(s) (NLP):** English
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+ - **License:** CC-BY 4.0
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+ - **Finetuned from model:** BERT base uncased
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+
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+ ### Model Sources
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+
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+ Underlying model is based on [BERT base-uncased](https://huggingface.co/google-bert/bert-base-uncased).
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+
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+ - **Repository:** [https://github.com/bltlab/query-ner](https://github.com/bltlab/query-ner)
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+ - **Paper:** Accepted at LREC-COLING Coming soon
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+
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+ ## Uses
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+
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+ ### Direct Use
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+
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+ Intended use is research purposes and e-commerce query segmentation.
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+
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+ ### Downstream Use
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+
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+ Potential downstream use cases include weighting entity spans, linking to knowledge bases, removing spans as a recovery strategy for null and low recall queries.
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+
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+ ### Out-of-Scope Use
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+
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+ 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.
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+
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+ ## Bias, Risks, and Limitations
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+
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+ See paper limitations section.
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+
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+ ## How to Get Started with the Model
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+
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+ See huggingface tutorials for token classification and access the model using AutoModelForTokenClassification.
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+ Note that we do some post processing to make use of only the first subtoken's tag unlike the inference API.
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ See paper for details.
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+
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+
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+ ### Training Procedure
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+
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+ See paper for details.
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+
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+ #### Training Hyperparameters
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+
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+ See paper for details.
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+
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+
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+ ## Evaluation
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+
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+ Evaluation details provided in the paper.
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+ Scoring was done using [SeqScore](https://github.com/bltlab/seqscore) using the conlleval repair method for invalid label transition sequences.
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ QueryNER test set: [https://huggingface.co/datasets/bltlab/queryner](https://huggingface.co/datasets/bltlab/queryner)
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+
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+
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+ #### Factors
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+ Evaluation is reported with micro-F1 at the entity level on the QueryNER test set.
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+ We used conlleval repair method for invalid label transitions.
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+
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+ #### Metrics
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+ We use micro-F1 at the entity level as this is fairly common practice for NER models.
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+
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+ ## Environmental Impact
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+ Rough estimate
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+
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+ - **Hardware Type:** 1 RTX 3090 GPU
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+ - **Hours used:** < 2 hours
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+ - **Cloud Provider:** Private
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+ - **Compute Region:** northamerica-northeast1
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+ - **Carbon Emitted:** 0.02
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+
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+
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+ ## Citation
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+
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+ Accepted at LREC-COLING coming soon
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+
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+ **BibTeX:**
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+
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+ Accepted at LREC-COLING coming soon
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+
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+
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+ ## Model Card Authors
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+
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+ Chester Palen-Michel (@cpalenmichel)[https://github.com/cpalenmichel]
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+
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+ ## Model Card Contact
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+
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+ Chester Palen-Michel (@cpalenmichel)[https://github.com/cpalenmichel]