Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+base_model: firqaaa/indo-sentence-bert-base
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+widget:
+- text: halaman 97 - 128 tidak ada , diulang halaman 65 - 96 , pembelian hari minggu
+    tanggal 24 desember sore sekitar jam 4 pembayaran menggunakan kartu atm bri bersamaan
+    dengan buku the puppeteer dan sirkus pohon
+- text: liverpool sukses di kandang tottenham
+- text: hai angga , untuk penerbitan tiket reschedule diharuskan melakukan pembayaran
+    dulu ya .
+- text: sedih kalau umat diprovokasi supaya saling membenci .
+- text: berada di lokasi strategis jalan merdeka , berseberangan agak ke samping bandung
+    indah plaza , tapat sebelah kanan jalan sebelum traffic light , parkir mobil cukup
+    luas . saus bumbu dan lain-lain disediakan cukup lengkap di lantai bawah . di
+    lantai atas suasana agak sepi . bakso cukup enak dan terjangkau harga nya tetapi
+    kuah relatif kurang dan porsi tidak terlalu besar
+pipeline_tag: text-classification
+inference: true
+model-index:
+- name: SetFit with firqaaa/indo-sentence-bert-base
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.7171717171717171
+      name: Accuracy
+    - type: precision
+      value: 0.7171717171717171
+      name: Precision
+    - type: recall
+      value: 0.7171717171717171
+      name: Recall
+    - type: f1
+      value: 0.7171717171717171
+      name: F1
+---
+
+# SetFit with firqaaa/indo-sentence-bert-base
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
+
+The model has been trained using an efficient few-shot learning technique that involves:
+
+1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
+2. Training a classification head with features from the fine-tuned Sentence Transformer.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [firqaaa/indo-sentence-bert-base](https://huggingface.co/firqaaa/indo-sentence-bert-base)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 3 classes
+<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
+- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
+- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
+
+### Model Labels
+| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 2     | <ul><li>'nasi campur terkenal di bandung , info nya nasi campur pertama di bandung . mengandung b2 . rasa standar nasi campur . ada babi merah , babi panggang , sate babi manis , bakso goreng , jerohan manis . layanan tidak ramah , maklum masih generasi tua yang beraksi . lokasi makan lumayan bersih tapi tidak berat'</li><li>'saya di cgv marvel city sby mau verifikasi sms redam , tapi di informasi telkomsel trobel , menyebalkan !'</li><li>'indonesia itu tipe yang kalau sudah down pasti susah bangkit lagi'</li></ul> |
+| 1     | <ul><li>'biru ada 4 , hijau ada 4 , merah ada 3 , kuning ada 3'</li><li>'baik terima kasih banyak'</li><li>'hai , ya , silakan kamu dapat mencoba lakukan pembayaran pdam di bukalapak .'</li></ul>                                                                                                                                                                                                                                                                                                                                      |
+| 0     | <ul><li>'nyaman banget kalau lagi nongkrong kenyang di warung upnormal . mulai dari pilihan menu nya yang serius banget digarap , dari pelayan2 nya yang kece , sampai ke interior nya yang super . rekomendasi banget deh kalau mau mengerjakan tugas , arisan , ulang tahun , reunian di sini .'</li><li>'conggo gallrely cafe di bandung utara . cafe nya sih okok saja . yang menarik adalah produksi meja dengan kayu-kayu yang panjang dan tebal khusus untuk meja makan .'</li><li>'terima kasih mas'</li></ul>                   |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy | Precision | Recall | F1     |
+|:--------|:---------|:----------|:-------|:-------|
+| **all** | 0.7172   | 0.7172    | 0.7172 | 0.7172 |
+
+## Uses
+
+### Direct Use for Inference
+
+First install the SetFit library:
+
+```bash
+pip install setfit
+```
+
+Then you can load this model and run inference.
+
+```python
+from setfit import SetFitModel
+
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("TRUEnder/setfit-indosentencebert-indonlusmsa-8-shot")
+# Run inference
+preds = model("liverpool sukses di kandang tottenham")
+```
+
+<!--
+### Downstream Use
+
+*List how someone could finetune this model on their own dataset.*
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 3   | 22.7917 | 61  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 8                     |
+| 1     | 8                     |
+| 2     | 8                     |
+
+### Training Hyperparameters
+- batch_size: (16, 2)
+- num_epochs: (2, 16)
+- max_steps: -1
+- sampling_strategy: oversampling
+- body_learning_rate: (2e-05, 1e-05)
+- head_learning_rate: 0.01
+- loss: CosineSimilarityLoss
+- distance_metric: cosine_distance
+- margin: 0.25
+- end_to_end: False
+- use_amp: False
+- warmup_proportion: 0.1
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch   | Step   | Training Loss | Validation Loss |
+|:-------:|:------:|:-------------:|:---------------:|
+| 0.0417  | 1      | 0.3908        | -               |
+| 0.0833  | 2      | 0.2962        | -               |
+| 0.125   | 3      | 0.2397        | -               |
+| 0.1667  | 4      | 0.3493        | -               |
+| 0.2083  | 5      | 0.2197        | -               |
+| 0.25    | 6      | 0.3782        | -               |
+| 0.2917  | 7      | 0.2341        | -               |
+| 0.3333  | 8      | 0.2166        | -               |
+| 0.375   | 9      | 0.3381        | -               |
+| 0.4167  | 10     | 0.1212        | -               |
+| 0.4583  | 11     | 0.1849        | -               |
+| 0.5     | 12     | 0.1796        | -               |
+| 0.5417  | 13     | 0.2027        | -               |
+| 0.5833  | 14     | 0.1824        | -               |
+| 0.625   | 15     | 0.1242        | -               |
+| 0.6667  | 16     | 0.1071        | -               |
+| 0.7083  | 17     | 0.1324        | -               |
+| 0.75    | 18     | 0.0667        | -               |
+| 0.7917  | 19     | 0.1095        | -               |
+| 0.8333  | 20     | 0.1277        | -               |
+| 0.875   | 21     | 0.0506        | -               |
+| 0.9167  | 22     | 0.0661        | -               |
+| 0.9583  | 23     | 0.0776        | -               |
+| 1.0     | 24     | 0.0371        | 0.2406          |
+| 1.0417  | 25     | 0.0652        | -               |
+| 1.0833  | 26     | 0.0698        | -               |
+| 1.125   | 27     | 0.0775        | -               |
+| 1.1667  | 28     | 0.052         | -               |
+| 1.2083  | 29     | 0.0399        | -               |
+| 1.25    | 30     | 0.0189        | -               |
+| 1.2917  | 31     | 0.0341        | -               |
+| 1.3333  | 32     | 0.0259        | -               |
+| 1.375   | 33     | 0.0844        | -               |
+| 1.4167  | 34     | 0.0322        | -               |
+| 1.4583  | 35     | 0.0186        | -               |
+| 1.5     | 36     | 0.0328        | -               |
+| 1.5417  | 37     | 0.0107        | -               |
+| 1.5833  | 38     | 0.027         | -               |
+| 1.625   | 39     | 0.0311        | -               |
+| 1.6667  | 40     | 0.0244        | -               |
+| 1.7083  | 41     | 0.0277        | -               |
+| 1.75    | 42     | 0.0132        | -               |
+| 1.7917  | 43     | 0.0153        | -               |
+| 1.8333  | 44     | 0.0147        | -               |
+| 1.875   | 45     | 0.0074        | -               |
+| 1.9167  | 46     | 0.0142        | -               |
+| 1.9583  | 47     | 0.0189        | -               |
+| **2.0** | **48** | **0.0095**    | **0.2139**      |
+
+* The bold row denotes the saved checkpoint.
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.2
+- PyTorch: 2.3.0+cu121
+- Datasets: 2.19.2
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+```bibtex
+@article{https://doi.org/10.48550/arxiv.2209.11055,
+    doi = {10.48550/ARXIV.2209.11055},
+    url = {https://arxiv.org/abs/2209.11055},
+    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
+    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+    title = {Efficient Few-Shot Learning Without Prompts},
+    publisher = {arXiv},
+    year = {2022},
+    copyright = {Creative Commons Attribution 4.0 International}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

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+{
+  "_name_or_path": "checkpoints/step_48",
+  "_num_labels": 5,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3",
+    "4": "LABEL_4"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
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+    "LABEL_1": 1,
+    "LABEL_2": 2,
+    "LABEL_3": 3,
+    "LABEL_4": 4
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 50000
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

config_setfit.json

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+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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model_head.pkl

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modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
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+  "padding_side": "right",
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+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}