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Upload DebertaV2ForSequenceClassification
Browse files- README.md +4 -4
- config.json +1 -1
- model-00001-of-00002.safetensors +1 -1
- model-00002-of-00002.safetensors +1 -1
README.md
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language:
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- en
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license: mit
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base_model: microsoft/deberta-v2-xxlarge
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tags:
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- nycu-112-2-datamining-hw2
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- generated_from_trainer
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datasets:
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- DandinPower/review_onlytitleandtext
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metrics:
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- name: deberta-v2-xxlarge-otat-recommened-hp
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results:
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- task:
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name: Text Classification
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type: text-classification
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dataset:
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name: DandinPower/review_onlytitleandtext
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type: DandinPower/review_onlytitleandtext
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metrics:
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-
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type: accuracy
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value: 0.6741428571428572
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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language:
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- en
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license: mit
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tags:
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- nycu-112-2-datamining-hw2
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- generated_from_trainer
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+
base_model: microsoft/deberta-v2-xxlarge
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datasets:
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- DandinPower/review_onlytitleandtext
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metrics:
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- name: deberta-v2-xxlarge-otat-recommened-hp
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results:
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- task:
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type: text-classification
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name: Text Classification
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dataset:
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name: DandinPower/review_onlytitleandtext
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type: DandinPower/review_onlytitleandtext
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metrics:
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- type: accuracy
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value: 0.6741428571428572
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name: Accuracy
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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config.json
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{
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"_name_or_path": "
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"architectures": [
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"DebertaV2ForSequenceClassification"
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],
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{
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"_name_or_path": "/mnt/nvme3n1p1/text-classification-exp/deberta-v2-xxlarge-otat-recommened-hp/checkpoint-1400",
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"architectures": [
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"DebertaV2ForSequenceClassification"
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],
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model-00001-of-00002.safetensors
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model-00002-of-00002.safetensors
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