Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 384,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "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: BAAI/bge-small-en-v1.5
+metrics:
+- accuracy
+widget:
+- text: approach affects entrepreneurship intention
+- text: innovation affects m & a success
+- text: total retail sales affects m & a success
+- text: stimulation of the sales staff in business organization affects entrepreneurship
+    intention
+- text: country-level economy affects ceo pay
+pipeline_tag: text-classification
+inference: true
+model-index:
+- name: SetFit with BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8117647058823529
+      name: Accuracy
+---
+
+# SetFit with BAAI/bge-small-en-v1.5
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
+- **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:** 2 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                                                                                                                                                                       |
+|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'oecd affects ceo pay'</li><li>'marketing and sales affects entrepreneurship intention'</li><li>'australian research affects entrepreneurship intention'</li></ul>     |
+| 1     | <ul><li>'academic performance affects entrepreneurship intention'</li><li>'collectivism affects entrepreneurship intention'</li><li>'responsibility affects ceo pay'</li></ul> |
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8118   |
+
+## 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("abehandlerorg/setfit")
+# Run inference
+preds = model("innovation affects m & a success")
+```
+
+<!--
+### 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   | 4   | 5.4661 | 13  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 164                   |
+| 1     | 175                   |
+
+### Training Hyperparameters
+- batch_size: (32, 32)
+- num_epochs: (4, 4)
+- 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: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0006 | 1    | 0.303         | -               |
+| 0.0276 | 50   | 0.2825        | -               |
+| 0.0553 | 100  | 0.2567        | -               |
+| 0.0829 | 150  | 0.2345        | -               |
+| 0.1106 | 200  | 0.2347        | -               |
+| 0.1382 | 250  | 0.1693        | -               |
+| 0.1658 | 300  | 0.0862        | -               |
+| 0.1935 | 350  | 0.0184        | -               |
+| 0.2211 | 400  | 0.0042        | -               |
+| 0.2488 | 450  | 0.0042        | -               |
+| 0.2764 | 500  | 0.0263        | -               |
+| 0.3040 | 550  | 0.0019        | -               |
+| 0.3317 | 600  | 0.0058        | -               |
+| 0.3593 | 650  | 0.0095        | -               |
+| 0.3870 | 700  | 0.0011        | -               |
+| 0.4146 | 750  | 0.0012        | -               |
+| 0.4422 | 800  | 0.0009        | -               |
+| 0.4699 | 850  | 0.0011        | -               |
+| 0.4975 | 900  | 0.001         | -               |
+| 0.5252 | 950  | 0.0215        | -               |
+| 0.5528 | 1000 | 0.0024        | -               |
+| 0.5804 | 1050 | 0.0034        | -               |
+| 0.6081 | 1100 | 0.0008        | -               |
+| 0.6357 | 1150 | 0.0161        | -               |
+| 0.6633 | 1200 | 0.0132        | -               |
+| 0.6910 | 1250 | 0.0009        | -               |
+| 0.7186 | 1300 | 0.0073        | -               |
+| 0.7463 | 1350 | 0.0089        | -               |
+| 0.7739 | 1400 | 0.0166        | -               |
+| 0.8015 | 1450 | 0.0005        | -               |
+| 0.8292 | 1500 | 0.0005        | -               |
+| 0.8568 | 1550 | 0.0006        | -               |
+| 0.8845 | 1600 | 0.0098        | -               |
+| 0.9121 | 1650 | 0.0005        | -               |
+| 0.9397 | 1700 | 0.0005        | -               |
+| 0.9674 | 1750 | 0.0263        | -               |
+| 0.9950 | 1800 | 0.0006        | -               |
+| 1.0227 | 1850 | 0.0005        | -               |
+| 1.0503 | 1900 | 0.0089        | -               |
+| 1.0779 | 1950 | 0.0074        | -               |
+| 1.1056 | 2000 | 0.0057        | -               |
+| 1.1332 | 2050 | 0.0006        | -               |
+| 1.1609 | 2100 | 0.0004        | -               |
+| 1.1885 | 2150 | 0.0004        | -               |
+| 1.2161 | 2200 | 0.0006        | -               |
+| 1.2438 | 2250 | 0.0005        | -               |
+| 1.2714 | 2300 | 0.0004        | -               |
+| 1.2991 | 2350 | 0.0088        | -               |
+| 1.3267 | 2400 | 0.0004        | -               |
+| 1.3543 | 2450 | 0.0005        | -               |
+| 1.3820 | 2500 | 0.0004        | -               |
+| 1.4096 | 2550 | 0.0118        | -               |
+| 1.4373 | 2600 | 0.0004        | -               |
+| 1.4649 | 2650 | 0.0149        | -               |
+| 1.4925 | 2700 | 0.0004        | -               |
+| 1.5202 | 2750 | 0.0004        | -               |
+| 1.5478 | 2800 | 0.0003        | -               |
+| 1.5755 | 2850 | 0.0004        | -               |
+| 1.6031 | 2900 | 0.0004        | -               |
+| 1.6307 | 2950 | 0.0136        | -               |
+| 1.6584 | 3000 | 0.0083        | -               |
+| 1.6860 | 3050 | 0.0094        | -               |
+| 1.7137 | 3100 | 0.0088        | -               |
+| 1.7413 | 3150 | 0.0004        | -               |
+| 1.7689 | 3200 | 0.0003        | -               |
+| 1.7966 | 3250 | 0.0004        | -               |
+| 1.8242 | 3300 | 0.0004        | -               |
+| 1.8519 | 3350 | 0.0101        | -               |
+| 1.8795 | 3400 | 0.0112        | -               |
+| 1.9071 | 3450 | 0.0003        | -               |
+| 1.9348 | 3500 | 0.0117        | -               |
+| 1.9624 | 3550 | 0.0003        | -               |
+| 1.9900 | 3600 | 0.0003        | -               |
+| 2.0177 | 3650 | 0.0003        | -               |
+| 2.0453 | 3700 | 0.0083        | -               |
+| 2.0730 | 3750 | 0.0003        | -               |
+| 2.1006 | 3800 | 0.0132        | -               |
+| 2.1282 | 3850 | 0.0003        | -               |
+| 2.1559 | 3900 | 0.0003        | -               |
+| 2.1835 | 3950 | 0.0003        | -               |
+| 2.2112 | 4000 | 0.0004        | -               |
+| 2.2388 | 4050 | 0.0003        | -               |
+| 2.2664 | 4100 | 0.0003        | -               |
+| 2.2941 | 4150 | 0.0003        | -               |
+| 2.3217 | 4200 | 0.0003        | -               |
+| 2.3494 | 4250 | 0.0003        | -               |
+| 2.3770 | 4300 | 0.0079        | -               |
+| 2.4046 | 4350 | 0.0003        | -               |
+| 2.4323 | 4400 | 0.0003        | -               |
+| 2.4599 | 4450 | 0.0003        | -               |
+| 2.4876 | 4500 | 0.0057        | -               |
+| 2.5152 | 4550 | 0.0003        | -               |
+| 2.5428 | 4600 | 0.0003        | -               |
+| 2.5705 | 4650 | 0.0003        | -               |
+| 2.5981 | 4700 | 0.0003        | -               |
+| 2.6258 | 4750 | 0.0003        | -               |
+| 2.6534 | 4800 | 0.0003        | -               |
+| 2.6810 | 4850 | 0.0003        | -               |
+| 2.7087 | 4900 | 0.0003        | -               |
+| 2.7363 | 4950 | 0.0003        | -               |
+| 2.7640 | 5000 | 0.0019        | -               |
+| 2.7916 | 5050 | 0.0157        | -               |
+| 2.8192 | 5100 | 0.0003        | -               |
+| 2.8469 | 5150 | 0.0098        | -               |
+| 2.8745 | 5200 | 0.0003        | -               |
+| 2.9022 | 5250 | 0.0117        | -               |
+| 2.9298 | 5300 | 0.0003        | -               |
+| 2.9574 | 5350 | 0.0003        | -               |
+| 2.9851 | 5400 | 0.0087        | -               |
+| 3.0127 | 5450 | 0.0002        | -               |
+| 3.0404 | 5500 | 0.0003        | -               |
+| 3.0680 | 5550 | 0.0085        | -               |
+| 3.0956 | 5600 | 0.0159        | -               |
+| 3.1233 | 5650 | 0.0003        | -               |
+| 3.1509 | 5700 | 0.0053        | -               |
+| 3.1786 | 5750 | 0.0003        | -               |
+| 3.2062 | 5800 | 0.0086        | -               |
+| 3.2338 | 5850 | 0.0002        | -               |
+| 3.2615 | 5900 | 0.0003        | -               |
+| 3.2891 | 5950 | 0.0055        | -               |
+| 3.3167 | 6000 | 0.0002        | -               |
+| 3.3444 | 6050 | 0.0092        | -               |
+| 3.3720 | 6100 | 0.0153        | -               |
+| 3.3997 | 6150 | 0.0002        | -               |
+| 3.4273 | 6200 | 0.0002        | -               |
+| 3.4549 | 6250 | 0.0002        | -               |
+| 3.4826 | 6300 | 0.0003        | -               |
+| 3.5102 | 6350 | 0.0101        | -               |
+| 3.5379 | 6400 | 0.0003        | -               |
+| 3.5655 | 6450 | 0.0003        | -               |
+| 3.5931 | 6500 | 0.0091        | -               |
+| 3.6208 | 6550 | 0.0002        | -               |
+| 3.6484 | 6600 | 0.0085        | -               |
+| 3.6761 | 6650 | 0.0003        | -               |
+| 3.7037 | 6700 | 0.0002        | -               |
+| 3.7313 | 6750 | 0.0002        | -               |
+| 3.7590 | 6800 | 0.0068        | -               |
+| 3.7866 | 6850 | 0.0003        | -               |
+| 3.8143 | 6900 | 0.0079        | -               |
+| 3.8419 | 6950 | 0.0175        | -               |
+| 3.8695 | 7000 | 0.0066        | -               |
+| 3.8972 | 7050 | 0.0003        | -               |
+| 3.9248 | 7100 | 0.0002        | -               |
+| 3.9525 | 7150 | 0.0065        | -               |
+| 3.9801 | 7200 | 0.0094        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.7.0
+- Transformers: 4.40.1
+- PyTorch: 2.2.1+cu121
+- Datasets: 2.19.1
+- 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

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.40.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,9 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.28.1",
+    "pytorch": "1.13.0+cu117"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

config_setfit.json

@@ -0,0 +1,4 @@
+{
+  "labels": null,
+  "normalize_embeddings": false
+}

model.safetensors

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+oid sha256:7ff4a5cc692963ca34ed6bcdd50b41b283618eaa7f67261e8cc597da4c77c9e5
+size 133462128

model_head.pkl

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+size 3935

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}