Add SetFit model

1_Pooling/config.json

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

README.md

@@ -0,0 +1,211 @@
+---
+library_name: setfit
+tags:
+- setfit
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+datasets:
+- hojzas/proj8-lab2
+metrics:
+- accuracy
+widget:
+- text: 'def first_with_given_key(iterable, key=lambda x: x):\n    keys_used = {}\n    for
+    item in iterable:\n        rp = repr(key(item))\n        if rp not in keys_used.keys():\n            keys_used[rp]
+    = repr(item)\n            yield item'
+- text: 'def first_with_given_key(iterable, key=lambda x: x):\n    keys=[]\n    for
+    i in iterable:\n        if key(i) not in keys:\n            yield i\n            keys.append(key(i))'
+- text: 'def first_with_given_key(lst, key = lambda x: x):\n    res = set()\n    for
+    i in lst:\n        if repr(key(i)) not in res:\n            res.add(repr(key(i)))\n            yield
+    i'
+- text: def first_with_given_key(iterable, key=repr):\n    used_keys = dict()\n    get_key
+    = return_key(key)\n    for index in iterable:\n        index_key = get_key(index)\n        if
+    index_key in used_keys.keys():\n            continue\n        try:\n            used_keys[hash(index_key)]
+    = repr(index)\n        except TypeError:\n            used_keys[repr(index_key)]
+    = repr(index)\n        yield index
+- text: 'def first_with_given_key(the_iterable, key=lambda x: x):\n    temp_keys=[]\n    for
+    i in range(len(the_iterable)):\n        if (key(the_iterable[i]) not in temp_keys):\n            temp_keys.append(key(the_iterable[i]))\n            yield
+    the_iterable[i]\n    del temp_keys'
+pipeline_tag: text-classification
+inference: true
+co2_eq_emissions:
+  emissions: 2.099245090500422
+  source: codecarbon
+  training_type: fine-tuning
+  on_cloud: false
+  cpu_model: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
+  ram_total_size: 251.49161911010742
+  hours_used: 0.006
+  hardware_used: 4 x NVIDIA RTX A5000
+base_model: sentence-transformers/all-mpnet-base-v2
+---
+
+# SetFit with sentence-transformers/all-mpnet-base-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [hojzas/proj8-lab2](https://huggingface.co/datasets/hojzas/proj8-lab2) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **Maximum Sequence Length:** 384 tokens
+- **Number of Classes:** 3 classes
+- **Training Dataset:** [hojzas/proj8-lab2](https://huggingface.co/datasets/hojzas/proj8-lab2)
+<!-- - **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>'def first_with_given_key(iterable, key=lambda x: x):\\n    keys_in_list = []\\n    for it in iterable:\\n    if key(it) not in keys_in_list:\\n        keys_in_list.append(key(it))\\n        yield it'</li><li>'def first_with_given_key(iterable, key=lambda value: value):\\n    it = iter(iterable)\\n    saved_keys = []\\n    while True:\\n        try:\\n            value = next(it)\\n            if key(value) not in saved_keys:\\n                saved_keys.append(key(value))\\n                yield value\\n        except StopIteration:\\n            break'</li><li>'def first_with_given_key(iterable, key=None):\\n    if key is None:\\n        key = lambda x: x\\n    item_list = []\\n    key_set = set()\\n    for item in iterable:\\n        generated_item = key(item)\\n        if generated_item not in item_list:\\n            item_list.append(generated_item)\\n            yield item'</li></ul>                                                                                                                                                                                             |
+| 2     | <ul><li>'def first_with_given_key(iterable, key=repr):\\n    prev_keys = {}\\n    lamb_key = lambda item: key(item)\\n    for obj in iterable:\\n        obj_key = lamb_key(obj)\\n        if(obj_key) in prev_keys.keys():\\n            continue\\n        try:\\n            prev_keys[hash(obj_key)] = repr(obj)\\n        except TypeError:\\n            prev_keys[repr(obj_key)] = repr(obj)\\n        yield obj'</li><li>'def first_with_given_key(iterable, key=repr):\\n    used_keys = dict()\\n    get_key = lambda index: key(index)\\n    for index in iterable:\\n        index_key = get_key(index)\\n        if index_key in used_keys.keys():\\n            continue\\n        try:\\n            used_keys[hash(index_key)] = repr(index)\\n        except TypeError:\\n            used_keys[repr(index_key)] = repr(index)\\n        yield index'</li><li>'def first_with_given_key(iterable, key=lambda x: x):\\n    keys_used = {}\\n    for item in iterable:\\n        rp = repr(key(item))\\n        if rp not in keys_used.keys():\\n            keys_used[rp] = repr(item)\\n            yield item'</li></ul> |
+| 1     | <ul><li>'def first_with_given_key(lst, key = lambda x: x):\\n    res = set()\\n    for i in lst:\\n        if repr(key(i)) not in res:\\n            res.add(repr(key(i)))\\n            yield i'</li><li>'def first_with_given_key(iterable, key=repr):\\n    set_of_keys = set()\\n    lambda_key = (lambda x: key(x))\\n    for item in iterable:\\n        key = lambda_key(item)\\n        try:\\n            key_for_set = hash(key)\\n        except TypeError:\\n            key_for_set = repr(key)\\n        if key_for_set in set_of_keys:\\n            continue\\n        set_of_keys.add(key_for_set)\\n        yield item'</li><li>'def first_with_given_key(iterable, key=None):\\n    if key is None:\\n        key = identity\\n    appeared_keys = set()\\n    for item in iterable:\\n        generated_key = key(item)\\n        if not generated_key.__hash__:\\n            generated_key = repr(generated_key)\\n        if generated_key not in appeared_keys:\\n            appeared_keys.add(generated_key)\\n            yield item'</li></ul>                                                                 |
+
+## 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("hojzas/proj8-lab2")
+# Run inference
+preds = model("def first_with_given_key(iterable, key=lambda x: x):\n    keys=[]\n    for i in iterable:\n        if key(i) not in keys:\n            yield i\n            keys.append(key(i))")
+```
+
+<!--
+### 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   | 43  | 92.2069 | 125 |
+
+| Label | Training Sample Count |
+|:------|:----------------------|
+| 0     | 13                    |
+| 1     | 8                     |
+| 2     | 8                     |
+
+### Training Hyperparameters
+- batch_size: (16, 16)
+- 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
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: False
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0137 | 1    | 0.4142        | -               |
+| 0.6849 | 50   | 0.0024        | -               |
+
+### Environmental Impact
+Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
+- **Carbon Emitted**: 0.002 kg of CO2
+- **Hours Used**: 0.006 hours
+
+### Training Hardware
+- **On Cloud**: No
+- **GPU Model**: 4 x NVIDIA RTX A5000
+- **CPU Model**: Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz
+- **RAM Size**: 251.49 GB
+
+### Framework Versions
+- Python: 3.10.12
+- SetFit: 1.0.3
+- Sentence Transformers: 2.2.2
+- Transformers: 4.36.1
+- PyTorch: 2.1.2+cu121
+- Datasets: 2.14.7
+- 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
+
+*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": "/home/xkrejc70/.cache/torch/sentence_transformers/sentence-transformers_all-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": "4.36.1",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,7 @@
+{
+  "__version__": {
+    "sentence_transformers": "2.0.0",
+    "transformers": "4.6.1",
+    "pytorch": "1.8.1"
+  }
+}

config_setfit.json

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

model.safetensors

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+oid sha256:3900270cbef096a79eb75e1e03078fd4cf7c9c5378731a9ce192cc0ba458a1fb
+size 437967672

model_head.pkl

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+oid sha256:6084e1ff38ffc5d2bcac6ad62745bb038d150b5c31d2da97291f1eb623f07e04
+size 19327

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"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 384,
+  "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

@@ -0,0 +1,72 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "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_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 512,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}