Push model using huggingface_hub.

1_Pooling/config.json

@@ -3,5 +3,7 @@
   "pooling_mode_cls_token": true,
   "pooling_mode_mean_tokens": false,
   "pooling_mode_max_tokens": false,
-  "pooling_mode_mean_sqrt_len_tokens": false
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false
 }

README.md

@@ -1,81 +1,206 @@
 ---
-license: apache-2.0
+library_name: setfit
 tags:
 - setfit
 - sentence-transformers
 - text-classification
+- generated_from_setfit_trainer
+datasets:
+- SetFit/sst2
+metrics:
+- accuracy
+widget:
+- text: a noble failure .
+- text: ms. seigner and mr. serrault bring fresh , unforced naturalism to their characters
+    .
+- text: 'nothing can detract from the affection of that moral favorite : friends will
+    be friends through thick and thin .'
+- text: confuses its message with an ultimate desire to please , and contorting itself
+    into an idea of expectation is the last thing any of these three actresses , nor
+    their characters , deserve .
+- text: despite its promising cast of characters , big trouble remains a loosely tied
+    series of vignettes which only prove that ` zany ' does n't necessarily mean `
+    funny . '
 pipeline_tag: text-classification
+inference: true
+base_model: BAAI/bge-small-en-v1.5
+model-index:
+- name: SetFit with BAAI/bge-small-en-v1.5
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: SetFit/sst2
+      type: SetFit/sst2
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.8841743119266054
+      name: Accuracy
 ---
 
-# moshew/bge-small-en-v1.5_setfit-sst2-english
+# SetFit with BAAI/bge-small-en-v1.5
 
-This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SetFit/sst2](https://huggingface.co/datasets/SetFit/sst2) dataset 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
 
-1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) ("BAAI/bge-small-en-v1.5") with contrastive learning.
+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.
 
-## Training code
+## Model Details
 
-```python
-from setfit import SetFitModel
+### 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 [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Classes:** 2 classes
+- **Training Dataset:** [SetFit/sst2](https://huggingface.co/datasets/SetFit/sst2)
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
 
-from datasets import load_dataset
-from setfit import SetFitModel, SetFitTrainer
+### Model Sources
 
-# Load a dataset from the Hugging Face Hub
-dataset = load_dataset("SetFit/sst2")
+- **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)
 
-# Upload Train and Test data
-num_classes = 2
-test_ds = dataset["test"]
-train_ds = dataset["train"]
+### Model Labels
+| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                        |
+|:------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 1     | <ul><li>'a stirring , funny and finally transporting re-imagining of beauty and the beast and 1930s horror films'</li><li>'this is a visually stunning rumination on love , memory , history and the war between art and commerce .'</li><li>"jonathan parker 's bartleby should have been the be-all-end-all of the modern-office anomie films ."</li></ul>                                                                                    |
+| 0     | <ul><li>'apparently reassembled from the cutting-room floor of any given daytime soap .'</li><li>"they presume their audience wo n't sit still for a sociology lesson , however entertainingly presented , so they trot out the conventional science-fiction elements of bug-eyed monsters and futuristic women in skimpy clothes ."</li><li>'a fan film that for the uninitiated plays better on video with the sound turned down .'</li></ul> |
 
-model = SetFitModel.from_pretrained("BAAI/bge-small-en-v1.5") 
-trainer = SetFitTrainer(model=model, train_dataset=train_ds, eval_dataset=test_ds)
+## Evaluation
 
-# Train and evaluate
-trainer.train()
-trainer.evaluate()['accuracy']
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.8842   |
 
-```
+## Uses
 
-## Usage
+### Direct Use for Inference
 
-To use this model for inference, first install the SetFit library:
+First install the SetFit library:
 
 ```bash
-python -m pip install setfit
+pip install setfit
 ```
 
-You can then run inference as follows:
+Then you can load this model and run inference.
 
 ```python
 from setfit import SetFitModel
 
-# Download from Hub and run inference
-model = SetFitModel.from_pretrained("moshew/bge-small-en-v1.5_setfit-sst2-english")
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("dkorat/bge-small-en-v1.5_setfit-sst2-english")
 # Run inference
-preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
+preds = model("a noble failure .")
 ```
 
-## Accuracy
-On SST-2 dev set:
-
-91.4%  SetFit
-
-88.4% (no Fine-Tuning) 
-
-## BibTeX entry and citation info
-
+<!--
+### 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   | 2   | 19.591 | 46  |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 479                   |
+| 1     | 521                   |
+
+### Training Hyperparameters
+- batch_size: (16, 2)
+- num_epochs: (1, 1)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 1
+- 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 |
+|:-----:|:----:|:-------------:|:---------------:|
+| 0.008 | 1    | 0.241         | -               |
+| 0.4   | 50   | 0.2525        | -               |
+| 0.8   | 100  | 0.0607        | -               |
+
+### Framework Versions
+- Python: 3.10.13
+- SetFit: 1.0.3
+- Sentence Transformers: 2.3.0
+- Transformers: 4.37.2
+- PyTorch: 2.1.2+cu121
+- Datasets: 2.16.1
+- Tokenizers: 0.15.1
+
+## 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}
+    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

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "/root/.cache/torch/sentence_transformers/BAAI_bge-small-en-v1.5/",
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
   "architectures": [
     "BertModel"
   ],
@@ -24,7 +24,7 @@
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.34.1",
+  "transformers_version": "4.37.2",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

config_setfit.json

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

model.safetensors

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+size 133462128

model_head.pkl

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-size 3919
+oid sha256:4068de7435b164f0b4153e1792c925ff7105d0ca1747c5679d2f3116173be49d
+size 4585

special_tokens_map.json

@@ -1,7 +1,37 @@
 {
-  "cls_token": "[CLS]",
-  "mask_token": "[MASK]",
-  "pad_token": "[PAD]",
-  "sep_token": "[SEP]",
-  "unk_token": "[UNK]"
+  "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
+  },
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+    "single_word": false
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
 }

tokenizer_config.json

@@ -46,7 +46,7 @@
   "do_basic_tokenize": true,
   "do_lower_case": true,
   "mask_token": "[MASK]",
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