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Add model card

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- base_model: bert-base-cased
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- library_name: peft
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  tags:
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- - base_model:adapter:bert-base-cased
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- - lora
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  - transformers
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
 
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- ## Model Card Authors [optional]
 
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- ## Model Card Contact
 
 
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- [More Information Needed]
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- ### Framework versions
 
 
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- - PEFT 0.17.1
 
 
 
 
 
 
 
 
 
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  ---
 
 
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  tags:
 
 
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  - transformers
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+ - token-classification
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+ - ner
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+ - bert
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+ - peft
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+ - lora
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+ - conll2003
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+ license: apache-2.0
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+ datasets:
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+ - conll2003
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+ language:
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+ - en
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+ pipeline_tag: token-classification
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+ authors:
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+ - Karan D Vasa (https://huggingface.co/starkdv123)
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  ---
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+ # BERT (base-cased) for CoNLL-2003 NER — LoRA Adapter (PEFT)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ This repository contains **LoRA adapter weights** trained on **CoNLL-2003** for BERT base cased.
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+ ## 📊 Reference result (merged model from same adapter)
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+ - **Entity Macro F1**: 0.9052
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+ ## Usage (attach adapter)
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
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+ from peft import PeftModel
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+ base = "bert-base-cased"
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+ adapter = "starkdv123/conll2003-bert-ner-lora"
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+ tok = AutoTokenizer.from_pretrained(base)
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+ base_model = AutoModelForTokenClassification.from_pretrained(base, num_labels=9)
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+ model = PeftModel.from_pretrained(base_model, adapter)
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+ clf = pipeline("token-classification", model=model, tokenizer=tok, aggregation_strategy="simple")
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+ clf("Chris Hoiles hit his 22nd homer for Baltimore.")
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+ ```
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+ ## Training summary
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+ * LoRA: r=8, alpha=16, dropout=0.1
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+ * Targets: [query, key, value, output.dense]
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+ * Epochs: 3, LR: 2e-4, warmup 0.1, batch 16/32
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+ ## Confusion Matrix
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+ ```
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+ LOC MISC O ORG PER
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+ LOC 384 6 35 43 5
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+ MISC 12 2138 80 100 33
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+ O 57 119 38060 58 21
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+ ORG 43 109 36 2304 11
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+ PER 1 27 18 22 2705
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+ ```