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README.md

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+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
+{}
+---
+
+# Model Card for Model ID
+
+E-commerce query segmentation model in English.
+
+
+## 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]. 
+
+
+- **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]