Push model using huggingface_hub.

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+  "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
+}

README.md

@@ -1,201 +1,267 @@
 ---
-library_name: transformers
-tags: []
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+metrics:
+- accuracy
+widget:
+- text: The ban, which went into effect in March 2019, was embraced by Trump following
+    a massacre that killed 58 people at a music festival in Las Vegas in which the
+    gunman used bump stocks.
+- text: 'Now Modi has made international headlines for yet another similarity: He’s
+    constructing a massive wall … but unlike Trump’s goal of keeping immigrants out,
+    Modi’s wall was built to hide the country’s poverty from the gold-plated American
+    president.'
+- text: 'Though banks have fled many low-income communities, there’s a post office
+    for almost every ZIP code in the country. '
+- text: The administration has stonewalled Congress during the impeachment proceedings
+    and other investigations, but the American public overwhelmingly wants the Trump
+    administration to comply with lawmakers.
+- text: The gun lobby has repeatedly claimed that using a gun in self-defense is a
+    common event, often going so far as to allege that Americans defend themselves
+    with guns millions of times a year.
+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: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.67003367003367
+      name: Accuracy
 ---
 
-# Model Card for Model ID
+# SetFit with BAAI/bge-small-en-v1.5
 
-<!-- Provide a quick summary of what the model is/does. -->
+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:** 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
+|:-------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| center | <ul><li>'A leading economist who vouched for Democratic presidential candidate Elizabeth Warren’s healthcare reform plan told Reuters on Thursday he doubts its staggering cost can be fully covered alongside her other government programs.'</li><li>'Labour leader Jeremy Corbyn unveiled his party’s election manifesto on Thursday, setting out radical plans to transform Britain with public sector pay rises, higher taxes on companies and a sweeping nationalisation of infrastructure.'</li><li>'Instagram will start blocking any hashtags spreading misinformation about vaccines, becoming the latest internet platform to crack down on bad health information.'</li></ul>                                                                                                                                                                                 |
+| right  | <ul><li>'Sanders praises the radical Green New Deal, champions a Medicare for All plan with a $34 trillion price tag, nods to abortion as a means of population control, and defends bread lines and Fidel Castro’s Cuba. '</li><li>'Since when did even conservative publications consider that it’s the right and moral thing to do to provide covering fire for an increasingly thuggish, openly hard-left, and borderline terroristic group which is less obviously to do with ‘racism’, but which has almost everything to do with smashing Western civilisation?'</li><li>'Local health officer\xa0Dr Rosana Salvaterra appeared to co-sign the demonstration,\xa0praising activists\xa0for wearing masks and claiming they obeyed social distancing protocols — although\xa0footage\xa0of the event strongly suggests that is\xa0not strictly accurate.'</li></ul> |
+| left   | <ul><li>'Activists planning to line California roadways with anti-vaccination billboards full of misinformation are paying for them through Facebook fundraisers, despite a platform-wide crackdown on such campaigns.'</li><li>'On Monday, as\xa0Common Dreams\xa0reported, Trump threatened to deploy federal forces to Chicago, Philadelphia, Detroit, Baltimore, and Oakland to confront Black Lives Matter protesters.'</li><li>"When the nation's highest civilian honor went to a right-wing media personality, it served as an oddly appropriate capstone to Trump's broader goals."</li></ul>                                                                                                                                                                                                                                                                    |
 
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
+## Evaluation
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.6700   |
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+### Direct Use for Inference
 
-### Direct Use
+First install the SetFit library:
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```bash
+pip install setfit
+```
 
-[More Information Needed]
+Then you can load this model and run inference.
 
-### Downstream Use [optional]
+```python
+from setfit import SetFitModel
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+# Download from the 🤗 Hub
+model = SetFitModel.from_pretrained("JordanTallon/Unifeed")
+# Run inference
+preds = model("Though banks have fled many low-income communities, there’s a post office for almost every ZIP code in the country. ")
+```
 
-[More Information Needed]
+<!--
+### Downstream Use
 
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+*List how someone could finetune this model on their own dataset.*
+-->
 
-[More Information Needed]
+<!--
+### Out-of-Scope Use
 
-## Bias, Risks, and Limitations
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+<!--
+## Bias, Risks and Limitations
 
-[More Information Needed]
+*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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
+### Training Set Metrics
+| Training set | Min | Median  | Max |
+|:-------------|:----|:--------|:----|
+| Word count   | 1   | 33.0139 | 195 |
+
+| Label  | Training Sample Count |
+|:-------|:----------------------|
+| center | 782                   |
+| left   | 780                   |
+| right  | 813                   |
+
+### Training Hyperparameters
+- batch_size: (64, 64)
+- num_epochs: (2, 2)
+- max_steps: -1
+- sampling_strategy: oversampling
+- num_iterations: 20
+- body_learning_rate: (2e-05, 2e-05)
+- head_learning_rate: 2e-05
+- 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 |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0007 | 1    | 0.2531        | -               |
+| 0.0337 | 50   | 0.253         | -               |
+| 0.0673 | 100  | 0.2491        | -               |
+| 0.1010 | 150  | 0.2592        | -               |
+| 0.1347 | 200  | 0.2476        | -               |
+| 0.1684 | 250  | 0.2282        | -               |
+| 0.2020 | 300  | 0.2222        | -               |
+| 0.2357 | 350  | 0.2196        | -               |
+| 0.2694 | 400  | 0.2199        | -               |
+| 0.3030 | 450  | 0.1821        | -               |
+| 0.3367 | 500  | 0.1819        | -               |
+| 0.3704 | 550  | 0.1327        | -               |
+| 0.4040 | 600  | 0.1193        | -               |
+| 0.4377 | 650  | 0.1652        | -               |
+| 0.4714 | 700  | 0.1059        | -               |
+| 0.5051 | 750  | 0.1141        | -               |
+| 0.5387 | 800  | 0.1103        | -               |
+| 0.5724 | 850  | 0.1138        | -               |
+| 0.6061 | 900  | 0.0894        | -               |
+| 0.6397 | 950  | 0.1138        | -               |
+| 0.6734 | 1000 | 0.11          | -               |
+| 0.7071 | 1050 | 0.1091        | -               |
+| 0.7407 | 1100 | 0.0804        | -               |
+| 0.7744 | 1150 | 0.1161        | -               |
+| 0.8081 | 1200 | 0.0715        | -               |
+| 0.8418 | 1250 | 0.1           | -               |
+| 0.8754 | 1300 | 0.0687        | -               |
+| 0.9091 | 1350 | 0.0488        | -               |
+| 0.9428 | 1400 | 0.0354        | -               |
+| 0.9764 | 1450 | 0.0244        | -               |
+| 1.0101 | 1500 | 0.02          | -               |
+| 1.0438 | 1550 | 0.0179        | -               |
+| 1.0774 | 1600 | 0.0219        | -               |
+| 1.1111 | 1650 | 0.0056        | -               |
+| 1.1448 | 1700 | 0.0169        | -               |
+| 1.1785 | 1750 | 0.0038        | -               |
+| 1.2121 | 1800 | 0.0139        | -               |
+| 1.2458 | 1850 | 0.0154        | -               |
+| 1.2795 | 1900 | 0.0118        | -               |
+| 1.3131 | 1950 | 0.0019        | -               |
+| 1.3468 | 2000 | 0.0016        | -               |
+| 1.3805 | 2050 | 0.0019        | -               |
+| 1.4141 | 2100 | 0.0016        | -               |
+| 1.4478 | 2150 | 0.0017        | -               |
+| 1.4815 | 2200 | 0.0011        | -               |
+| 1.5152 | 2250 | 0.0013        | -               |
+| 1.5488 | 2300 | 0.0123        | -               |
+| 1.5825 | 2350 | 0.0014        | -               |
+| 1.6162 | 2400 | 0.0013        | -               |
+| 1.6498 | 2450 | 0.001         | -               |
+| 1.6835 | 2500 | 0.0042        | -               |
+| 1.7172 | 2550 | 0.0017        | -               |
+| 1.7508 | 2600 | 0.0027        | -               |
+| 1.7845 | 2650 | 0.0016        | -               |
+| 1.8182 | 2700 | 0.0011        | -               |
+| 1.8519 | 2750 | 0.0014        | -               |
+| 1.8855 | 2800 | 0.0012        | -               |
+| 1.9192 | 2850 | 0.0012        | -               |
+| 1.9529 | 2900 | 0.0009        | -               |
+| 1.9865 | 2950 | 0.001         | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.2.2
+- Transformers: 4.35.2
+- PyTorch: 2.1.0+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}
+}
+```
+
+<!--
+## 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
 
-[More Information Needed]
-
-
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -1,35 +1,31 @@
 {
-  "_name_or_path": "distilbert-base-uncased",
-  "activation": "gelu",
+  "_name_or_path": "/root/.cache/torch/sentence_transformers/BAAI_bge-small-en-v1.5/",
   "architectures": [
-    "DistilBertForSequenceClassification"
+    "BertModel"
   ],
-  "attention_dropout": 0.1,
-  "dim": 768,
-  "dropout": 0.1,
-  "hidden_dim": 3072,
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
   "id2label": {
-    "0": "LABEL_0",
-    "1": "LABEL_1",
-    "2": "LABEL_2"
+    "0": "LABEL_0"
   },
   "initializer_range": 0.02,
+  "intermediate_size": 1536,
   "label2id": {
-    "LABEL_0": 0,
-    "LABEL_1": 1,
-    "LABEL_2": 2
+    "LABEL_0": 0
   },
+  "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,
-  "model_type": "distilbert",
-  "n_heads": 12,
-  "n_layers": 6,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
   "pad_token_id": 0,
-  "problem_type": "single_label_classification",
-  "qa_dropout": 0.1,
-  "seq_classif_dropout": 0.2,
-  "sinusoidal_pos_embds": false,
-  "tie_weights_": true,
+  "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.37.1",
+  "transformers_version": "4.35.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
   "vocab_size": 30522
 }

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.2.2",
+    "transformers": "4.28.1",
+    "pytorch": "1.13.0+cu117"
+  }
+}

config_setfit.json

@@ -0,0 +1,8 @@
+{
+  "normalize_embeddings": false,
+  "labels": [
+    "center",
+    "left",
+    "right"
+  ]
+}

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d319c271a248dbc0e880b559ecfc84e1f7b60ab4846fa2497052e5101f3d180f
-size 267835644
+oid sha256:a81bd8924fa00d82337cceb9a0c535bc7271d064c81d2d9105d506b6050a4ac2
+size 133462128

model_head.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a34c5cf85c7314e1103b5e1d923ef70f5f27a9866401f60d9b5a3f5b808f2605
+size 10143

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
+}

tokenizer.json

@@ -2,14 +2,12 @@
   "version": "1.0",
   "truncation": {
     "direction": "Right",
-    "max_length": 128,
+    "max_length": 512,
     "strategy": "LongestFirst",
     "stride": 0
   },
   "padding": {
-    "strategy": {
-      "Fixed": 128
-    },
+    "strategy": "BatchLongest",
     "direction": "Right",
     "pad_to_multiple_of": null,
     "pad_id": 0,

tokenizer_config.json

@@ -43,13 +43,15 @@
   },
   "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": "DistilBertTokenizer",
+  "tokenizer_class": "BertTokenizer",
   "unk_token": "[UNK]"
 }