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README.md
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license: apache-2.0
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---
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license: apache-2.0
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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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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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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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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tokenizer = AutoTokenizer.from_pretrained('InstructPLM/MPNN-ProGen2-xlarge-CATH42', trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained('InstructPLM/MPNN-ProGen2-xlarge-CATH42', trust_remote_code=True)
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model.cuda().eval()
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model.requires_grad_(False)
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batch = tokenizer('Fast-PETase.pyd|1MQTNPYARGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPESRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWHSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSQNAKQFLEIKGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTAVSDFRTANCS2',return_tensors='pt').to(device=model.device)
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labels = batch.input_ids.masked_fill((1-batch.attention_mask).bool(), -100)
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labels[:, :tokenizer.n_queries+1] = -100
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batch["labels"] = labels
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with torch.no_grad():
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with torch.cuda.amp.autocast(dtype=torch.float16):
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output = model(**batch)
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print(output.loss.item())
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batch = tokenizer('Fast-PETase.pyd|1',return_tensors='pt').to(device=model.device)
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tokens_batch = model.generate(
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**batch,
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do_sample=True,
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temperature=0.8,
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max_length=512+tokenizer.n_queries,
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min_new_tokens=5,
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top_p=0.9,
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num_return_sequences=5,
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pad_token_id=0,
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repetition_penalty=1.0,
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bad_words_ids=[[3]]
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)
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texts = tokenizer.batch_decode(tokens_batch)
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def truncate_seq(text):
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bos = text.find('1')
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eos = text.find('2')
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if eos > bos and bos >= 0:
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return text[bos+1:eos]
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else:
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return text[bos+1:]
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print([truncate_seq(t) for t in texts])
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# Ref. Seq
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# 'MQTNPYARGPNPTAASLEASAGPFTVRSFTVSRPSGYGAGTVYYPTNAGGTVGAIAIVPGYTARQSSIKWWGPRLASHGFVVITIDTNSTLDQPESRSSQQMAALRQVASLNGTSSSPIYGKVDTARMGVMGWSMGGGGSLISAANNPSLKAAAPQAPWHSSTNFSSVTVPTLIFACENDSIAPVNSSALPIYDSMSQNAKQFLEIKGGSHSCANSGNSNQALIGKKGVAWMKRFMDNDTRYSTFACENPNSTAVSDFRTANCS'
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# Designed seq:
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# 'METNPFHRGPDPTCASLEAGAGPFNVQSFRVDRPLGFGAGTVFYPTDAGGQVPAIAIAPGFTQTQSSVMWYGPRLASHGFVVIVIDTISTFDNPDSRSAQLLAALDQVANLNSNASSPIYGKVDTTRQAVMGHSMGGGGSLISAMNNPSLKAAAPMAPWHVSTNFSAVQVPTFIIGAENDTIAPVASHSIPFYNSIPSSLPKAYMELAGASHLAPNSSNPTIAKYSISWLKRFVDNDTRYEQFLCPAPTSTALISEYRDTCPY',
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# 'EETNPYSKGPDPTAASLEASAGPFTVQSFSVARPLGFGAGTVYYPTDAGGKVGAIAVVPGYTDTQGSIRWWGPRLASHGFVVMTIDTISSYDQPDSRSAQLMAALDQLANLNSTSSSPIYNKVDTTRQAVMGHSMGGGGSLISAMNNPNLKAAIPMAPWHSSTNFSSVKVPTMILGAERDTVAPVSSHAEPFYNSLPSSTPKAYLELKGASHFFPNTTNTPTFAKSVLAWLKRFVDNDTRYEQFLCPGPTSTDLTDYRNTCPY',
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# 'SETNPYIKGPDPTAASLEASAGAFTVQSFTVSRPTGFGAGTVYYPTDAGGRVGAIAIVPGYTATQSSIKWWGPRLASHGFVVMTIDTNSTYDQPDSRANQLMAALDQLTNLNSTRSSPIYGKVDTTRQGVMGHSMGGGGSLIAAQDNPNLKAAIPLAPWHSSSNFSSVTVPTLIIGAQNDTVAPVSSHSIPFYTSLPSSLDKAYLELNGASHFAPNSSNTTIAKYSISWLKRFIDNDTRYEQFLCPPPSGSALISEYRNTCPY',
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# 'EETWPYHRGPDPTAASLEASAGPFTVQSFTVARPLGFGAGTVYYPTDAGGRVGAVAVVPGYTQTQSAIRWWGPRLASHGFVVMTIDTISTFDQPDSRSAQLLAALDQLAVLNSTRSSPIYNKVDTTRQGVMGHSMGGGGSLISAMNNPSLKAAVPLAPWHASTNFSNVQVPTLIIGASDDTTASVTTHSIPFYNSIPSSVPKAYLELQGQSHFCPNTSNTTIAKYSISWLKRFIDNDTRYDQFLCPPPNGSAISDYRSTCPH',
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# 'METNPFIRGPNPTAASLEASAGPFQVSSFSVARPVGFGAGTVYYPTDAGGQVPAIAIAPGFTQTQASVKWYGPRLASHGFVVIVIDTNSTLDNPDSRSAQLLAALDQVSTLNSSSSSPIYGKVDTTRQGVMGHSMGGGGSLISAQNNPALKAAIPLAPWHVSTDFSGVTVPTLIIGAENDTVAPVGTHAEPFYNSIPSSTPKAYLELNNASHFAPNTSNTTIAKYSIAWLKRFVDNDTRYDQFLCPAPNGNAIQDYRDTCPH'
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#
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```
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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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[More Information Needed]
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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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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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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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[More Information Needed]
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**APA:**
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[More Information Needed]
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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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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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