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

@@ -0,0 +1,294 @@
+---
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: "Deputy Finance Ministers from the Group\nof 10 leading western industrialised\
+    \ countries met here to\ndiscuss the world debt crisis, trade imbalances and currency\n\
+    stability today following last month's Paris monetary accord,\nsources close to\
+    \ the talks said.\n    The officials met at the offices of the International\n\
+    Monetary Fund (IMF) to discuss broad aspects of world monetary\npolicy in preparation\
+    \ for the IMF's interim committee meeting\nin Washington in April.\n    The talks\
+    \ were the first high-level international review of\nthe monetary situation since\
+    \ the accord last month reached by\nthe U.S., West Germany, France, Britain, Japan\
+    \ and Canada to\nstabilise world currency markets at around present levels\nfollowing\
+    \ the 40 pct slide in the dollar since mid-1985.\n    Other countries represented\
+    \ at today's talks were Italy,\nwhich refused to attend last month's meeting on\
+    \ the grounds\nthat it was being excluded from the real discussions, the\nNetherlands,\
+    \ Belgium and Switzerland.\n    Many of the officials had met earlier today and\
+    \ yesterday\nwithin the framework of the Organisation for Economic\nCooperation\
+    \ and Development (OECD) to review the slow progress\nbeing made in cutting the\
+    \ record 170 billion dlr U.S. Trade\ndeficit and persuading West Germany and Japan\
+    \ to open their\neconomies to more foreign imports.\n Reuter\n"
+- text: "Oper shr 69 cts vs 83 cts\n    Oper net 35.9 mln vs 42.4 mln\n    Revs 798.9\
+    \ mln vs 659.2 mln\n    Avg shrs 52.0 mln vs 50.9 mln\n    Nine mths\n    Oper\
+    \ shr 2.38 dlrs vs 2.75 dlrs\n    Oper net 123.3 mln vs 135.6 mln\n    Revs 2.31\
+    \ billion vs 1.86 billion\n    Avg shrs 51.8 mln vs 49.3 mln\n    NOTE: Net excludes\
+    \ losses from discontinued operations of\nnil vs 16.1 mln dlrs in quarter and\
+    \ 227.5 mln dlrs vs 42.7 mln\ndlrs in nine mths.\n    Quarter net includes gains\
+    \ from sale of aircraft of two mln\ndlrs vs 6,200,000 dlrs.\n Reuter\n"
+- text: "The National Association of Wheat\nGrowers, NAWG, board of directors is scheduled\
+    \ to meet\nSecretary of State George Schultz and Undersecretary of State\nAllen\
+    \ Wallis to discuss the Department's current role in farm\ntrade policy, the association\
+    \ said.\n    NAWG President Jim Miller said in a statement that the\norganization\
+    \ wanted to convey to Secretary Schultz the\nimportance that exports hold for\
+    \ U.S. agriculture and the\ndegree to which farmers are dependent upon favorable\
+    \ State\nDepartment trade policies to remain profitable.\n    \"Foreign policy\
+    \ decisions of the U.S. State Department have\nin the past severely hampered our\
+    \ efforts to move our product\nto overseas markets,\" he said.\n    Miller noted\
+    \ Secretary Schultz is scheduled to meet next\nmonth with representatives of the\
+    \ Soviet Union, and the NAWG\n\"wanted to be certain the secretary was aware of\
+    \ our concerns\nregarding the reopening of wheat trade with the Soviet Union.\"\
+    \n    The annual spring NAWG board of directors meeting is held\nin Washington\
+    \ to allow grower-leaders from around the country\nto meet with their state congressional\
+    \ delegations and members\nof the executive branch.\n    The purpose is to discuss\
+    \ the current situation for\nproducing and marketing wheat and help set the legislative\
+    \ and\nregulatory agenda for the coming year, the NAWG statement said.\n Reuter\n"
+- text: "The Bank of France is likely to cut its\nmoney market intervention rate by\
+    \ up to a quarter point at the\nstart of next week.  This follows a steady decline\
+    \ in the call\nmoney rate over the past 10 days and signals from the Finance\n\
+    Ministry that the time is ripe for a fall, dealers said.\n    The call money rate\
+    \ peaked at just above nine pct ahead of\nthe meeting of finance ministers from\
+    \ the Group of Five\nindustrial countries and Canada on February 22, which restored\n\
+    considerable stability to foreign exchanges after several weeks\nof turbulence.\n\
+    \    The call money rate dropped to around 8-3/8 pct on February\n23, the day\
+    \ after the Paris accord, and then edged steadily\ndown to eight pct on February\
+    \ 27 and 7-3/4 pct on March 3,\nwhere it has now stabilised.\n    Dealers said\
+    \ the Bank of France intervened to absorb\nliquidity to hold the rate at 7-3/4\
+    \ pct.\n    While call money has dropped by well over a percentage\npoint, the\
+    \ Bank of France's money market intervention rate has\nremained unchanged since\
+    \ January 2, when it was raised to eight\npct from 7-1/4 pct in a bid to stop\
+    \ a franc slide.\n    The seven-day repurchase rate has also been unchanged at\n\
+    8-3/4 since it was raised by a half-point on January 5.\n    The Bank of France\
+    \ has begun using the seven-day repurchase\nrate to set an upper indicator for\
+    \ money market rates, while\nusing the intervention rate to set the floor.\n \
+    \   Sources close to Finance Minister Edouard Balladur said\nthat he would be\
+    \ happy to see an interest rate cut, and dealers\nsaid any fall in the intervention\
+    \ rate was most likely to come\nwhen the Bank of France buys first category paper\
+    \ next Monday,\nalthough an earlier cut could not be excluded.\n   A cut in the\
+    \ seven-day repurchase rate could come as early\nas tomorrow morning, banking\
+    \ sources said.\n    They said recent high interest rates have encouraged an\n\
+    acceleration in foreign funds returning to France, discouraging\nthe authorities\
+    \ from making a hasty rate cut. But they also\npointed out that money supply is\
+    \ broadly back on target, giving\nscope for a small fall in rates.\n    M-3 money\
+    \ supply, the government's key aggregate, finished\n1986 within the government's\
+    \ three to five pct growth target,\nrising 4.6 pct compared with seven pct in\
+    \ 1985.\n REUTER\n"
+- text: "The French 1986 current account balance\nof payments surplus has been revised\
+    \ slightly upwards to 25.8\nbillion francs from the 25.4 billion franc figure\
+    \ announced\nlast month, the Finance Ministry said.\n    This compares with a\
+    \ 1.5 billion deficit in 1985, and while\nit is the first surplus since 1979,\
+    \ is substantially lower than\nthe 50 billion surplus forecast by the previous\
+    \ socialist\ngovernment before they lost office in March last year.\n    Net long-term\
+    \ capital outflows rose sharply to 70.5 billion\nfrancs last year from 8.8 billion\
+    \ in 1985, largely due to a\nmajor program of foreign debt repayment, the ministry\
+    \ said.\n    In the fourth quarter alone the unadjusted surplus rose to\n14.1\
+    \ billion francs from 6.6 billion the previous quarter, but\nthe adjusted surplus\
+    \ fell to 7.4 billion from 9.1 billion.\n    Fourth quarter medium and long-term\
+    \ foreign debt repayments\nexceeded new credits by 11 billion francs.\n REUTER\n"
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: false
+base_model: sentence-transformers/paraphrase-mpnet-base-v2
+model-index:
+- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+  results:
+  - task:
+      type: text-classification
+      name: Text Classification
+    dataset:
+      name: Unknown
+      type: unknown
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.785234899328859
+      name: Accuracy
+---
+
+# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier 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:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
+- **Classification head:** a OneVsRestClassifier instance
+- **Maximum Sequence Length:** 512 tokens
+<!-- - **Number of Classes:** Unknown -->
+<!-- - **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)
+
+## Evaluation
+
+### Metrics
+| Label   | Accuracy |
+|:--------|:---------|
+| **all** | 0.7852   |
+
+## 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("ardi555/setfit_reuters21578_reducedto15")
+# Run inference
+preds = model("Oper shr 69 cts vs 83 cts
+    Oper net 35.9 mln vs 42.4 mln
+    Revs 798.9 mln vs 659.2 mln
+    Avg shrs 52.0 mln vs 50.9 mln
+    Nine mths
+    Oper shr 2.38 dlrs vs 2.75 dlrs
+    Oper net 123.3 mln vs 135.6 mln
+    Revs 2.31 billion vs 1.86 billion
+    Avg shrs 51.8 mln vs 49.3 mln
+    NOTE: Net excludes losses from discontinued operations of
+nil vs 16.1 mln dlrs in quarter and 227.5 mln dlrs vs 42.7 mln
+dlrs in nine mths.
+    Quarter net includes gains from sale of aircraft of two mln
+dlrs vs 6,200,000 dlrs.
+ Reuter
+")
+```
+
+<!--
+### 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   | 1   | 181.1067 | 788 |
+
+### Training Hyperparameters
+- batch_size: (8, 8)
+- num_epochs: (1, 1)
+- 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
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0013 | 1    | 0.4971        | -               |
+| 0.0667 | 50   | 0.1826        | -               |
+| 0.1333 | 100  | 0.1223        | -               |
+| 0.2    | 150  | 0.0699        | -               |
+| 0.2667 | 200  | 0.0712        | -               |
+| 0.3333 | 250  | 0.0646        | -               |
+| 0.4    | 300  | 0.055         | -               |
+| 0.4667 | 350  | 0.0611        | -               |
+| 0.5333 | 400  | 0.053         | -               |
+| 0.6    | 450  | 0.0555        | -               |
+| 0.6667 | 500  | 0.0475        | -               |
+| 0.7333 | 550  | 0.0716        | -               |
+| 0.8    | 600  | 0.0587        | -               |
+| 0.8667 | 650  | 0.0571        | -               |
+| 0.9333 | 700  | 0.0436        | -               |
+| 1.0    | 750  | 0.0505        | -               |
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.1.0
+- Sentence Transformers: 3.2.1
+- Transformers: 4.42.2
+- PyTorch: 2.5.1+cu121
+- Datasets: 3.1.0
+- 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 +1,24 @@
+{
+  "_name_or_path": "sentence-transformers/paraphrase-mpnet-base-v2",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.2.1",
+    "transformers": "4.42.2",
+    "pytorch": "2.5.1+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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+version https://git-lfs.github.com/spec/v1
+oid sha256:7283c2c4adf958606810ae90f960e1fa6955f7b7f8ed21ac020057965ead9277
+size 437967672

model_head.pkl

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+oid sha256:abca1b11332474de71449f111ac1e43956fbe4748fa07db9fd91a701d9ad7fd8
+size 98212

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

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

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "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

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+      "special": true
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+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}