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1_Pooling/config.json

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+{
+  "word_embedding_dimension": 384,
+  "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

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----
-license: apache-2.0
----
+---
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: Edelweiss:Downgrade Wipro to 'Hold', says Edelweiss
+- text: Overweight:Morgan Stanley upgrades Axis Bank to Overweight; ups target price
+- text: 'downside:Expect more downside in the IT, pharma stocks: Sandeep Wagle'
+- text: 'Barclays:Infusion of additional $1 trillion to India''s GDP to create new
+    midcap leaders: Barclays'
+- text: focus:Jaypee, Reliance Group stocks in focus ahead of UP results
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: false
+base_model: sentence-transformers/all-MiniLM-L6-v2
+---
+
+# SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
+
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Aspect Based Sentiment Analysis (ABSA). This SetFit model uses [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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. In particular, this model is in charge of filtering aspect span candidates.
+
+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.
+
+This model was trained within the context of a larger system for ABSA, which looks like so:
+
+1. Use a spaCy model to select possible aspect span candidates.
+2. **Use this SetFit model to filter these possible aspect span candidates.**
+3. Use a SetFit model to classify the filtered aspect span candidates.
+
+## Model Details
+
+### Model Description
+- **Model Type:** SetFit
+- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
+- **spaCy Model:** en_core_web_sm
+- **SetFitABSA Aspect Model:** [/scratch/project_2006600/fin_experiment/models/setfit-finance-aspect](https://huggingface.co//scratch/project_2006600/fin_experiment/models/setfit-finance-aspect)
+- **SetFitABSA Polarity Model:** [/scratch/project_2006600/fin_experiment/models/setfit-finance-polarity](https://huggingface.co//scratch/project_2006600/fin_experiment/models/setfit-finance-polarity)
+- **Maximum Sequence Length:** 256 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                                                                                                                                                                                                                                                           |
+|:----------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| aspect    | <ul><li>'Sebi:Ponzi schemes: Sebi seeks quarterly meetings of state panels'</li><li>'Vodafone:European shares steady, pegged back by Vodafone'</li><li>'European shares:European shares steady, pegged back by Vodafone'</li></ul>                                 |
+| no aspect | <ul><li>'Ponzi schemes:Ponzi schemes: Sebi seeks quarterly meetings of state panels'</li><li>'meetings:Ponzi schemes: Sebi seeks quarterly meetings of state panels'</li><li>'state panels:Ponzi schemes: Sebi seeks quarterly meetings of state panels'</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 AbsaModel
+
+# Download from the 🤗 Hub
+model = AbsaModel.from_pretrained(
+    "/scratch/project_2006600/fin_experiment/models/setfit-finance-aspect",
+    "/scratch/project_2006600/fin_experiment/models/setfit-finance-polarity",
+)
+# Run inference
+preds = model("The food was great, but the venue is just way too busy.")
+```
+
+<!--
+### 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   | 10.9446 | 24  |
+
+| Label     | Training Sample Count |
+|:----------|:----------------------|
+| no aspect | 3387                  |
+| aspect    | 1988                  |
+
+### Training Hyperparameters
+- batch_size: (64, 64)
+- num_epochs: (2, 2)
+- 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: True
+- warmup_proportion: 0.1
+- l2_weight: 0.01
+- seed: 42
+- eval_max_steps: -1
+- load_best_model_at_end: True
+
+### Training Results
+| Epoch  | Step | Training Loss | Validation Loss |
+|:------:|:----:|:-------------:|:---------------:|
+| 0.0000 | 1    | 0.3294        | -               |
+| 0.0004 | 50   | 0.3505        | -               |
+| 0.0008 | 100  | 0.3428        | 0.3418          |
+| 0.0012 | 150  | 0.3407        | -               |
+| 0.0017 | 200  | 0.3284        | 0.3288          |
+| 0.0021 | 250  | 0.3232        | -               |
+| 0.0025 | 300  | 0.3092        | 0.3087          |
+| 0.0029 | 350  | 0.3047        | -               |
+| 0.0033 | 400  | 0.2906        | 0.2849          |
+| 0.0037 | 450  | 0.2782        | -               |
+| 0.0041 | 500  | 0.2695        | 0.2667          |
+| 0.0046 | 550  | 0.2625        | -               |
+| 0.0050 | 600  | 0.2601        | 0.2574          |
+| 0.0054 | 650  | 0.2541        | -               |
+| 0.0058 | 700  | 0.2554        | 0.2539          |
+| 0.0062 | 750  | 0.2552        | -               |
+| 0.0066 | 800  | 0.2537        | 0.2529          |
+| 0.0071 | 850  | 0.2555        | -               |
+| 0.0075 | 900  | 0.2544        | 0.2518          |
+| 0.0079 | 950  | 0.2528        | -               |
+| 0.0083 | 1000 | 0.2536        | 0.2506          |
+| 0.0087 | 1050 | 0.251         | -               |
+| 0.0091 | 1100 | 0.2508        | 0.2493          |
+| 0.0095 | 1150 | 0.2504        | -               |
+| 0.0100 | 1200 | 0.2497        | 0.2480          |
+| 0.0104 | 1250 | 0.2484        | -               |
+| 0.0108 | 1300 | 0.2476        | 0.2463          |
+| 0.0112 | 1350 | 0.2468        | -               |
+| 0.0116 | 1400 | 0.2454        | 0.2440          |
+| 0.0120 | 1450 | 0.245         | -               |
+| 0.0124 | 1500 | 0.2427        | 0.2409          |
+| 0.0129 | 1550 | 0.2401        | -               |
+| 0.0133 | 1600 | 0.2412        | 0.2361          |
+| 0.0137 | 1650 | 0.2373        | -               |
+| 0.0141 | 1700 | 0.2325        | 0.2279          |
+| 0.0145 | 1750 | 0.2305        | -               |
+| 0.0149 | 1800 | 0.2245        | 0.2144          |
+| 0.0153 | 1850 | 0.2186        | -               |
+| 0.0158 | 1900 | 0.2089        | 0.1916          |
+| 0.0162 | 1950 | 0.1935        | -               |
+| 0.0166 | 2000 | 0.1761        | 0.1500          |
+| 0.0170 | 2050 | 0.1477        | -               |
+| 0.0174 | 2100 | 0.1395        | 0.1287          |
+| 0.0178 | 2150 | 0.1315        | -               |
+| 0.0183 | 2200 | 0.1231        | 0.1178          |
+| 0.0187 | 2250 | 0.1172        | -               |
+| 0.0191 | 2300 | 0.1082        | 0.1085          |
+| 0.0195 | 2350 | 0.1005        | -               |
+| 0.0199 | 2400 | 0.0999        | 0.1058          |
+| 0.0203 | 2450 | 0.0881        | -               |
+| 0.0207 | 2500 | 0.0899        | 0.1009          |
+| 0.0212 | 2550 | 0.0851        | -               |
+| 0.0216 | 2600 | 0.0829        | 0.0986          |
+| 0.0220 | 2650 | 0.0779        | -               |
+| 0.0224 | 2700 | 0.0773        | 0.0968          |
+| 0.0228 | 2750 | 0.0731        | -               |
+| 0.0232 | 2800 | 0.0687        | 0.0944          |
+| 0.0236 | 2850 | 0.0673        | -               |
+| 0.0241 | 2900 | 0.0667        | 0.0934          |
+| 0.0245 | 2950 | 0.0617        | -               |
+| 0.0249 | 3000 | 0.0619        | 0.0928          |
+| 0.0253 | 3050 | 0.0577        | -               |
+| 0.0257 | 3100 | 0.0559        | 0.0927          |
+| 0.0261 | 3150 | 0.0588        | -               |
+| 0.0265 | 3200 | 0.0557        | 0.0939          |
+| 0.0270 | 3250 | 0.0526        | -               |
+| 0.0274 | 3300 | 0.0519        | 0.0913          |
+| 0.0278 | 3350 | 0.0498        | -               |
+| 0.0282 | 3400 | 0.0454        | 0.0902          |
+| 0.0286 | 3450 | 0.0442        | -               |
+| 0.0290 | 3500 | 0.0425        | 0.0892          |
+| 0.0295 | 3550 | 0.0391        | -               |
+| 0.0299 | 3600 | 0.0369        | 0.0879          |
+| 0.0303 | 3650 | 0.0384        | -               |
+| 0.0307 | 3700 | 0.033         | 0.0890          |
+| 0.0311 | 3750 | 0.0341        | -               |
+| 0.0315 | 3800 | 0.0364        | 0.0896          |
+| 0.0319 | 3850 | 0.0347        | -               |
+| 0.0324 | 3900 | 0.0321        | 0.0891          |
+
+### Framework Versions
+- Python: 3.11.11
+- SetFit: 1.1.0
+- Sentence Transformers: 3.3.1
+- spaCy: 3.7.5
+- Transformers: 4.42.1
+- PyTorch: 2.5.1+cu124
+- Datasets: 3.2.0
+- 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

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+{
+  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.42.1",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

config_setfit.json

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+{
+  "span_context": 0,
+  "spacy_model": "en_core_web_sm",
+  "normalize_embeddings": false,
+  "labels": [
+    "no aspect",
+    "aspect"
+  ]
+}

model.safetensors

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+oid sha256:9f7241887e38f04c953c97a748c8d2dbf515b78010cafdfc024ca4d155a4df7b
+size 90864192

model_head.pkl

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+size 3919

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

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+{
+  "max_seq_length": 256,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "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

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+{
+  "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
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+      "lstrip": false,
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+      "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]",
+  "max_length": 128,
+  "model_max_length": 256,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}