Push model using huggingface_hub.
Browse files- README.md +28 -24
- config.json +1 -1
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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@@ -12,18 +12,19 @@ metrics:
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- recall
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- f1
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widget:
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- text:
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pipeline_tag: text-classification
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inference: true
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model-index:
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split: test
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metrics:
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- type: accuracy
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value: 0.
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name: Accuracy
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- type: precision
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value: 0.
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name: Precision
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- type: recall
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value: 0.
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name: Recall
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- type: f1
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value: 0.
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name: F1
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---
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@@ -88,9 +89,9 @@ The model has been trained using an efficient few-shot learning technique that i
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## Evaluation
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### Metrics
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| Label | Accuracy | Precision | Recall | F1
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| **all** | 0.
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## Uses
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("TRUEnder/setfit-indosentencebert-indonlusmsa-8-shot")
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# Run inference
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preds = model("
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```
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<!--
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@@ -152,7 +153,7 @@ preds = model("liverpool sukses di kandang tottenham")
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### Training Hyperparameters
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- batch_size: (16, 2)
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- num_epochs: (
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- max_steps: -1
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- sampling_strategy: oversampling
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- body_learning_rate: (2e-05, 1e-05)
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:-------:|:------:|:-------------:|:---------------:|
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* The bold row denotes the saved checkpoint.
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### Framework Versions
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- recall
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- f1
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widget:
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- text: menang di 3 pilgub pulau jawa , ppp optimis dilirik jadi cawapres
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- text: salah satu tempat makan di bandung yang menjadi favorit karena masakan yang
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enak dan pelayanan yang menyenangkan . suasana tempat makan yang serius dipikirkan
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sehingga mempunyai cita rasa yang menarik dipadu makanan yang enak membuat betah
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dan berpikir untuk datang lagi . ini asli rekomendasi banget untuk didatangi dan
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dicoba masakan nya . bandung memang surga nya makanan dan bebek garang benar-benar
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gahar .
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- text: makanan nya lumayan enak , sup iga nya nikmat . anak-anak juga bisa sambil
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bermain karena ada playground nya .
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- text: menukarkan 5 poin mendapatkan 1 kupon undian , siapa tahu tahun ini menjadi
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terberuntungan ku mendapatkan salah satu hadiah nya .
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- text: kalau bukan karena pak jokowi , indonesia pasti sudah tidak ada bentuk nya
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, terima kasih , pak .
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pipeline_tag: text-classification
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inference: true
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model-index:
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split: test
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metrics:
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- type: accuracy
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value: 0.75
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name: Accuracy
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- type: precision
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value: 0.75
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name: Precision
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- type: recall
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value: 0.75
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name: Recall
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- type: f1
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value: 0.75
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name: F1
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---
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## Evaluation
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### Metrics
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| Label | Accuracy | Precision | Recall | F1 |
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|:--------|:---------|:----------|:-------|:-----|
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| **all** | 0.75 | 0.75 | 0.75 | 0.75 |
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## Uses
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# Download from the 🤗 Hub
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model = SetFitModel.from_pretrained("TRUEnder/setfit-indosentencebert-indonlusmsa-8-shot")
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# Run inference
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preds = model("menang di 3 pilgub pulau jawa , ppp optimis dilirik jadi cawapres")
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```
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<!--
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### Training Hyperparameters
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- batch_size: (16, 2)
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- num_epochs: (6, 16)
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- max_steps: -1
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- sampling_strategy: oversampling
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- body_learning_rate: (2e-05, 1e-05)
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:-------:|:------:|:-------------:|:---------------:|
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| 1.0 | 24 | 0.0498 | 0.1801 |
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| 2.0 | 48 | 0.0032 | 0.1736 |
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| 3.0 | 72 | 0.0014 | 0.1703 |
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| **4.0** | **96** | **0.001** | **0.1696** |
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| 5.0 | 120 | 0.0009 | 0.1712 |
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| 6.0 | 144 | 0.0008 | 0.1713 |
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* The bold row denotes the saved checkpoint.
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### Framework Versions
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config.json
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{
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"_name_or_path": "checkpoints/
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"_num_labels": 5,
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"architectures": [
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"BertModel"
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{
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"_name_or_path": "checkpoints/step_96",
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"_num_labels": 5,
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"architectures": [
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"BertModel"
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 497787752
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size 497787752
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model_head.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 19327
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version https://git-lfs.github.com/spec/v1
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size 19327
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