Add SetFit ABSA model
Browse files- README.md +213 -171
- config.json +1 -1
- config_setfit.json +2 -2
- model.safetensors +1 -1
- model_head.pkl +1 -1
README.md
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metrics:
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- accuracy
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widget:
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pipeline_tag: text-classification
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inference: false
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base_model: firqaaa/indo-sentence-bert-base
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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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---
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- **Sentence Transformer body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base)
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- **spaCy Model:** id_core_news_trf
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-
- **SetFitABSA Aspect Model:** [firqaaa/indo-setfit-absa-
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-
- **SetFitABSA Polarity Model:** [firqaaa/indo-setfit-absa-
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Classes:** 2 classes
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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### Metrics
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| Label | Accuracy |
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|:--------|:---------|
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| **all** | 0.
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## Uses
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@@ -104,8 +106,8 @@ from setfit import AbsaModel
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# Download from the 🤗 Hub
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model = AbsaModel.from_pretrained(
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"firqaaa/indo-setfit-absa-
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"firqaaa/indo-setfit-absa-
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)
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# Run inference
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preds = model("The food was great, but the venue is just way too busy.")
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:--------|:----|
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| Word count | 2 |
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| Label | Training Sample Count |
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|:----------|:----------------------|
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| no aspect |
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| aspect |
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### Training Hyperparameters
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- batch_size: (
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- num_epochs: (1, 1)
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- max_steps: -1
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- sampling_strategy: oversampling
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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* The bold row denotes the saved checkpoint.
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### Framework Versions
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metrics:
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- accuracy
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widget:
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+
- text: timur:unggul di atas tetangga di jalan 6 timur, taj mahal juga sangat sebanding,
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dalam kualitas makanan, dengan baluchi yang terlalu dipuji (dan kurang layak).
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- text: makanan:saya sangat merekomendasikan cafe st bart's untuk makanan mereka,
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suasana dan layanan yang luar biasa melayani
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- text: terong parmesan:parmesan terung juga enak, dan teman saya yang besar di manhattan
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metakan bahwa tidak ada orang yang pantas mendapatkan ziti panggang yang lebih
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enak dengan saus daging terong parmesan
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- text: tuna lelehan:kami memesan tuna lelehan - itu datang dengan keluar keju yang
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ha membuat sandwich tuna daging tuna
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- text: manhattan metakan:parmesan terung juga enak, dan teman saya yang besar di
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manhattan metakan bahwa tidak ada orang yang pantas mendapatkan ziti panggang
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yang lebih enak dengan saus daging ziti panggang dengan saus daging
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pipeline_tag: text-classification
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inference: false
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base_model: firqaaa/indo-sentence-bert-base
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split: test
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metrics:
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- type: accuracy
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value: 0.9087072065030483
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name: Accuracy
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---
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- **Sentence Transformer body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base)
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
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- **spaCy Model:** id_core_news_trf
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+
- **SetFitABSA Aspect Model:** [firqaaa/indo-setfit-absa-bert-base-restaurants-aspect](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-aspect)
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- **SetFitABSA Polarity Model:** [firqaaa/indo-setfit-absa-bert-base-restaurants-polarity](https://huggingface.co/firqaaa/indo-setfit-absa-bert-base-restaurants-polarity)
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Classes:** 2 classes
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
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### Metrics
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| Label | Accuracy |
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|:--------|:---------|
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| **all** | 0.9087 |
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## Uses
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# Download from the 🤗 Hub
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model = AbsaModel.from_pretrained(
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"firqaaa/indo-setfit-absa-bert-base-restaurants-aspect",
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"firqaaa/indo-setfit-absa-bert-base-restaurants-polarity",
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)
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# Run inference
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preds = model("The food was great, but the venue is just way too busy.")
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### Training Set Metrics
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| Training set | Min | Median | Max |
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|:-------------|:----|:--------|:----|
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+
| Word count | 2 | 19.7819 | 59 |
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| Label | Training Sample Count |
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|:----------|:----------------------|
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| no aspect | 2939 |
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| aspect | 1468 |
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### Training Hyperparameters
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- batch_size: (16, 16)
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- num_epochs: (1, 1)
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- max_steps: -1
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- sampling_strategy: oversampling
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### Training Results
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| Epoch | Step | Training Loss | Validation Loss |
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|:----------:|:--------:|:-------------:|:---------------:|
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| 0.0000 | 1 | 0.3135 | - |
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| 0.0001 | 50 | 0.3401 | - |
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| 0.0001 | 100 | 0.3212 | - |
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| 0.0002 | 150 | 0.3641 | - |
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| 0.0003 | 200 | 0.3317 | - |
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| 0.0004 | 250 | 0.2809 | - |
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| 0.0004 | 300 | 0.2446 | - |
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| 0.0005 | 350 | 0.284 | - |
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| 0.0006 | 400 | 0.3257 | - |
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| 0.0007 | 450 | 0.2996 | - |
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| 0.0007 | 500 | 0.209 | 0.295 |
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| 0.0008 | 550 | 0.2121 | - |
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| 0.0009 | 600 | 0.2204 | - |
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| 0.0010 | 650 | 0.3023 | - |
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| 0.0010 | 700 | 0.3253 | - |
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| 0.0011 | 750 | 0.233 | - |
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| 0.0012 | 800 | 0.3131 | - |
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| 0.0013 | 850 | 0.2873 | - |
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| 0.0013 | 900 | 0.2028 | - |
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| 0.0014 | 950 | 0.2608 | - |
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| 0.0015 | 1000 | 0.2842 | 0.2696 |
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| 0.0016 | 1050 | 0.2297 | - |
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| 0.0016 | 1100 | 0.266 | - |
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| 0.0017 | 1150 | 0.2771 | - |
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| 0.0018 | 1200 | 0.2347 | - |
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| 0.0019 | 1250 | 0.2539 | - |
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| 0.0019 | 1300 | 0.3409 | - |
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| 0.0020 | 1350 | 0.2925 | - |
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| 0.0021 | 1400 | 0.2608 | - |
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| 0.0021 | 1450 | 0.2792 | - |
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| 0.0022 | 1500 | 0.261 | 0.2636 |
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| 0.0023 | 1550 | 0.2596 | - |
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| 0.0024 | 1600 | 0.2563 | - |
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| 0.0024 | 1650 | 0.2329 | - |
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| 0.0025 | 1700 | 0.2954 | - |
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| 0.0026 | 1750 | 0.3329 | - |
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| 0.0027 | 1800 | 0.2138 | - |
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| 0.0027 | 1850 | 0.2591 | - |
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| 0.0028 | 1900 | 0.268 | - |
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| 0.0029 | 1950 | 0.2144 | - |
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| 0.0030 | 2000 | 0.2361 | 0.2586 |
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| 0.0030 | 2050 | 0.2322 | - |
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| 0.0031 | 2100 | 0.2646 | - |
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| 0.0032 | 2150 | 0.2018 | - |
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| 0.0033 | 2200 | 0.2579 | - |
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| 0.0033 | 2250 | 0.2501 | - |
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| 0.0034 | 2300 | 0.2657 | - |
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| 0.0035 | 2350 | 0.2272 | - |
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| 0.0036 | 2400 | 0.2383 | - |
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| 0.0036 | 2450 | 0.2615 | - |
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| 0.0037 | 2500 | 0.2818 | 0.2554 |
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| 0.0038 | 2550 | 0.2616 | - |
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| 0.0039 | 2600 | 0.2225 | - |
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| 0.0039 | 2650 | 0.2749 | - |
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| 0.0040 | 2700 | 0.2572 | - |
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| 0.0041 | 2750 | 0.2729 | - |
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| 0.0041 | 2800 | 0.2559 | - |
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| 0.0042 | 2850 | 0.2363 | - |
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| 0.0043 | 2900 | 0.2518 | - |
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| 0.0044 | 2950 | 0.1948 | - |
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| 0.0044 | 3000 | 0.2842 | 0.2538 |
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| 0.0045 | 3050 | 0.2243 | - |
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| 0.0046 | 3100 | 0.2186 | - |
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| 0.0047 | 3150 | 0.2829 | - |
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| 0.0047 | 3200 | 0.2101 | - |
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| 0.0048 | 3250 | 0.2156 | - |
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| 0.0049 | 3300 | 0.2539 | - |
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| 0.0050 | 3350 | 0.3005 | - |
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| 0.0050 | 3400 | 0.2699 | - |
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| 0.0051 | 3450 | 0.2431 | - |
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| 0.0052 | 3500 | 0.2931 | 0.2515 |
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| 0.0053 | 3550 | 0.2032 | - |
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| 0.0053 | 3600 | 0.2451 | - |
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| 0.0054 | 3650 | 0.2419 | - |
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| 0.0055 | 3700 | 0.2267 | - |
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| 0.0056 | 3750 | 0.2945 | - |
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| 0.0056 | 3800 | 0.2689 | - |
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| 0.0057 | 3850 | 0.2596 | - |
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| 0.0058 | 3900 | 0.2978 | - |
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| 0.0059 | 3950 | 0.2876 | - |
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| 252 |
+
| 0.0059 | 4000 | 0.2484 | 0.2482 |
|
| 253 |
+
| 0.0060 | 4050 | 0.2698 | - |
|
| 254 |
+
| 0.0061 | 4100 | 0.2155 | - |
|
| 255 |
+
| 0.0061 | 4150 | 0.2474 | - |
|
| 256 |
+
| 0.0062 | 4200 | 0.2683 | - |
|
| 257 |
+
| 0.0063 | 4250 | 0.2979 | - |
|
| 258 |
+
| 0.0064 | 4300 | 0.2866 | - |
|
| 259 |
+
| 0.0064 | 4350 | 0.2604 | - |
|
| 260 |
+
| 0.0065 | 4400 | 0.1989 | - |
|
| 261 |
+
| 0.0066 | 4450 | 0.2708 | - |
|
| 262 |
+
| 0.0067 | 4500 | 0.2705 | 0.2407 |
|
| 263 |
+
| 0.0067 | 4550 | 0.2144 | - |
|
| 264 |
+
| 0.0068 | 4600 | 0.2503 | - |
|
| 265 |
+
| 0.0069 | 4650 | 0.2193 | - |
|
| 266 |
+
| 0.0070 | 4700 | 0.1796 | - |
|
| 267 |
+
| 0.0070 | 4750 | 0.2384 | - |
|
| 268 |
+
| 0.0071 | 4800 | 0.1933 | - |
|
| 269 |
+
| 0.0072 | 4850 | 0.2248 | - |
|
| 270 |
+
| 0.0073 | 4900 | 0.22 | - |
|
| 271 |
+
| 0.0073 | 4950 | 0.2052 | - |
|
| 272 |
+
| 0.0074 | 5000 | 0.2314 | 0.224 |
|
| 273 |
+
| 0.0075 | 5050 | 0.2279 | - |
|
| 274 |
+
| 0.0076 | 5100 | 0.2198 | - |
|
| 275 |
+
| 0.0076 | 5150 | 0.2332 | - |
|
| 276 |
+
| 0.0077 | 5200 | 0.1666 | - |
|
| 277 |
+
| 0.0078 | 5250 | 0.1949 | - |
|
| 278 |
+
| 0.0079 | 5300 | 0.1802 | - |
|
| 279 |
+
| 0.0079 | 5350 | 0.2496 | - |
|
| 280 |
+
| 0.0080 | 5400 | 0.2399 | - |
|
| 281 |
+
| 0.0081 | 5450 | 0.2042 | - |
|
| 282 |
+
| 0.0082 | 5500 | 0.1859 | 0.2077 |
|
| 283 |
+
| 0.0082 | 5550 | 0.2216 | - |
|
| 284 |
+
| 0.0083 | 5600 | 0.1227 | - |
|
| 285 |
+
| 0.0084 | 5650 | 0.2351 | - |
|
| 286 |
+
| 0.0084 | 5700 | 0.2735 | - |
|
| 287 |
+
| 0.0085 | 5750 | 0.1008 | - |
|
| 288 |
+
| 0.0086 | 5800 | 0.1568 | - |
|
| 289 |
+
| 0.0087 | 5850 | 0.1211 | - |
|
| 290 |
+
| 0.0087 | 5900 | 0.0903 | - |
|
| 291 |
+
| 0.0088 | 5950 | 0.1473 | - |
|
| 292 |
+
| 0.0089 | 6000 | 0.1167 | 0.1877 |
|
| 293 |
+
| 0.0090 | 6050 | 0.206 | - |
|
| 294 |
+
| 0.0090 | 6100 | 0.2392 | - |
|
| 295 |
+
| 0.0091 | 6150 | 0.116 | - |
|
| 296 |
+
| 0.0092 | 6200 | 0.1493 | - |
|
| 297 |
+
| 0.0093 | 6250 | 0.1373 | - |
|
| 298 |
+
| 0.0093 | 6300 | 0.1163 | - |
|
| 299 |
+
| 0.0094 | 6350 | 0.0669 | - |
|
| 300 |
+
| 0.0095 | 6400 | 0.0756 | - |
|
| 301 |
+
| 0.0096 | 6450 | 0.0788 | - |
|
| 302 |
+
| 0.0096 | 6500 | 0.1816 | 0.1838 |
|
| 303 |
+
| 0.0097 | 6550 | 0.1288 | - |
|
| 304 |
+
| 0.0098 | 6600 | 0.0946 | - |
|
| 305 |
+
| 0.0099 | 6650 | 0.1374 | - |
|
| 306 |
+
| 0.0099 | 6700 | 0.2167 | - |
|
| 307 |
+
| 0.0100 | 6750 | 0.0759 | - |
|
| 308 |
+
| 0.0101 | 6800 | 0.1543 | - |
|
| 309 |
+
| 0.0102 | 6850 | 0.0573 | - |
|
| 310 |
+
| 0.0102 | 6900 | 0.1169 | - |
|
| 311 |
+
| 0.0103 | 6950 | 0.0294 | - |
|
| 312 |
+
| **0.0104** | **7000** | **0.1241** | **0.1769** |
|
| 313 |
+
| 0.0104 | 7050 | 0.0803 | - |
|
| 314 |
+
| 0.0105 | 7100 | 0.0139 | - |
|
| 315 |
+
| 0.0106 | 7150 | 0.01 | - |
|
| 316 |
+
| 0.0107 | 7200 | 0.0502 | - |
|
| 317 |
+
| 0.0107 | 7250 | 0.0647 | - |
|
| 318 |
+
| 0.0108 | 7300 | 0.0117 | - |
|
| 319 |
+
| 0.0109 | 7350 | 0.0894 | - |
|
| 320 |
+
| 0.0110 | 7400 | 0.0101 | - |
|
| 321 |
+
| 0.0110 | 7450 | 0.0066 | - |
|
| 322 |
+
| 0.0111 | 7500 | 0.0347 | 0.1899 |
|
| 323 |
+
| 0.0112 | 7550 | 0.0893 | - |
|
| 324 |
+
| 0.0113 | 7600 | 0.0127 | - |
|
| 325 |
+
| 0.0113 | 7650 | 0.1285 | - |
|
| 326 |
+
| 0.0114 | 7700 | 0.0049 | - |
|
| 327 |
+
| 0.0115 | 7750 | 0.0571 | - |
|
| 328 |
+
| 0.0116 | 7800 | 0.0068 | - |
|
| 329 |
+
| 0.0116 | 7850 | 0.0586 | - |
|
| 330 |
+
| 0.0117 | 7900 | 0.0788 | - |
|
| 331 |
+
| 0.0118 | 7950 | 0.0655 | - |
|
| 332 |
+
| 0.0119 | 8000 | 0.0052 | 0.1807 |
|
| 333 |
+
| 0.0119 | 8050 | 0.0849 | - |
|
| 334 |
+
| 0.0120 | 8100 | 0.0133 | - |
|
| 335 |
+
| 0.0121 | 8150 | 0.0445 | - |
|
| 336 |
+
| 0.0122 | 8200 | 0.0118 | - |
|
| 337 |
+
| 0.0122 | 8250 | 0.0118 | - |
|
| 338 |
+
| 0.0123 | 8300 | 0.063 | - |
|
| 339 |
+
| 0.0124 | 8350 | 0.0751 | - |
|
| 340 |
+
| 0.0124 | 8400 | 0.058 | - |
|
| 341 |
+
| 0.0125 | 8450 | 0.002 | - |
|
| 342 |
+
| 0.0126 | 8500 | 0.0058 | 0.1804 |
|
| 343 |
+
| 0.0127 | 8550 | 0.0675 | - |
|
| 344 |
+
| 0.0127 | 8600 | 0.0067 | - |
|
| 345 |
+
| 0.0128 | 8650 | 0.0087 | - |
|
| 346 |
+
| 0.0129 | 8700 | 0.0028 | - |
|
| 347 |
+
| 0.0130 | 8750 | 0.0626 | - |
|
| 348 |
+
| 0.0130 | 8800 | 0.0563 | - |
|
| 349 |
+
| 0.0131 | 8850 | 0.0012 | - |
|
| 350 |
+
| 0.0132 | 8900 | 0.0067 | - |
|
| 351 |
+
| 0.0133 | 8950 | 0.0011 | - |
|
| 352 |
+
| 0.0133 | 9000 | 0.0105 | 0.189 |
|
| 353 |
+
| 0.0134 | 9050 | 0.101 | - |
|
| 354 |
+
| 0.0135 | 9100 | 0.1162 | - |
|
| 355 |
+
| 0.0136 | 9150 | 0.0593 | - |
|
| 356 |
+
| 0.0136 | 9200 | 0.0004 | - |
|
| 357 |
+
| 0.0137 | 9250 | 0.0012 | - |
|
| 358 |
+
| 0.0138 | 9300 | 0.0022 | - |
|
| 359 |
+
| 0.0139 | 9350 | 0.0033 | - |
|
| 360 |
+
| 0.0139 | 9400 | 0.0025 | - |
|
| 361 |
+
| 0.0140 | 9450 | 0.0578 | - |
|
| 362 |
+
| 0.0141 | 9500 | 0.0012 | 0.1967 |
|
| 363 |
|
| 364 |
* The bold row denotes the saved checkpoint.
|
| 365 |
### Framework Versions
|
config.json
CHANGED
|
@@ -1,5 +1,5 @@
|
|
| 1 |
{
|
| 2 |
-
"_name_or_path": "models/
|
| 3 |
"_num_labels": 5,
|
| 4 |
"architectures": [
|
| 5 |
"BertModel"
|
|
|
|
| 1 |
{
|
| 2 |
+
"_name_or_path": "models/step_7000/",
|
| 3 |
"_num_labels": 5,
|
| 4 |
"architectures": [
|
| 5 |
"BertModel"
|
config_setfit.json
CHANGED
|
@@ -1,9 +1,9 @@
|
|
| 1 |
{
|
|
|
|
|
|
|
| 2 |
"labels": [
|
| 3 |
"no aspect",
|
| 4 |
"aspect"
|
| 5 |
],
|
| 6 |
-
"spacy_model": "id_core_news_trf",
|
| 7 |
-
"span_context": 0,
|
| 8 |
"normalize_embeddings": false
|
| 9 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"span_context": 0,
|
| 3 |
+
"spacy_model": "id_core_news_trf",
|
| 4 |
"labels": [
|
| 5 |
"no aspect",
|
| 6 |
"aspect"
|
| 7 |
],
|
|
|
|
|
|
|
| 8 |
"normalize_embeddings": false
|
| 9 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 497787752
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b29233c2bea6ca2fe88b694613d04013b0b67623a2dbf4494aee27ac45ff818b
|
| 3 |
size 497787752
|
model_head.pkl
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 6991
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:74777165396345dcca555ac0cb5187b2cedef4dd6bc091916cc1be89543210a9
|
| 3 |
size 6991
|