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1_Pooling/config.json

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+{
+  "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

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----
-license: apache-2.0
----
+---
+tags:
+- setfit
+- absa
+- sentence-transformers
+- text-classification
+- generated_from_setfit_trainer
+widget:
+- text: 'Room Buzz: Alstom, DRL,:Dealing Room Buzz: Alstom, DRL, Raymond, Titan'
+- text: 'like Cummins, Voltas and Engineers India:Capital goods names like Cummins,
+    Voltas and Engineers India to fetch returns: Manish Sonthalia'
+- text: DCM Shriram Consolidated rallies 17%:DCM Shriram Consolidated rallies 17%,
+    hits 52-week high on plans to reward shareholders
+- text: 'Deepak Mohoni, trendwatchindia.com:Tinplate is certainly a hold: Deepak Mohoni,
+    trendwatchindia.com'
+- text: Dollar flatlines ahead of:Dollar flatlines ahead of Janet Yellen, Mario Draghi
+    at Jackson Hole
+metrics:
+- accuracy
+pipeline_tag: text-classification
+library_name: setfit
+inference: false
+base_model: sentence-transformers/all-mpnet-base-v2
+---
+
+# SetFit Polarity Model with sentence-transformers/all-mpnet-base-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-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. In particular, this model is in charge of classifying aspect polarities.
+
+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 a SetFit model to filter these possible aspect span candidates.
+3. **Use this SetFit model to classify the filtered aspect span candidates.**
+
+## 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
+- **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:** 384 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                                                                                                                                                                                                                                                                                                                                                       |
+|:---------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| neutral  | <ul><li>'Ponzi schemes: Sebi seeks quarterly meetings:Ponzi schemes: Sebi seeks quarterly meetings of state panels'</li><li>'European shares steady, pegged:European shares steady, pegged back by Vodafone'</li><li>'Bajaj Auto Q2 net at:Bajaj Auto Q2 net at Rs 591 crore'</li></ul>                                                                        |
+| negative | <ul><li>'pegged back by Vodafone:European shares steady, pegged back by Vodafone'</li><li>'M&M Finance plunges 8.5%:M&M Finance plunges 8.5% as brokers cut target price post Q3 results'</li><li>"' rating on Tata Motors; prefer Hero:Have 'sell' rating on Tata Motors; prefer Hero MotoCorp among auto stocks: Harendra Kumar"</li></ul>                   |
+| positive | <ul><li>"Buy' on Wipro with target of:Maintain 'Buy' on Wipro with target of Rs 528: Sharekhan"</li><li>"Motors; prefer Hero MotoCorp among auto stocks:Have 'sell' rating on Tata Motors; prefer Hero MotoCorp among auto stocks: Harendra Kumar"</li><li>'Servalakshmi Paper debuts at over:Servalakshmi Paper debuts at over 3 pc premium on BSE'</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   | 6   | 14.2133 | 29  |
+
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| negative | 536                   |
+| neutral  | 749                   |
+| positive | 703                   |
+
+### 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    | 0.2969        | -               |
+| 0.0025 | 50   | 0.3201        | -               |
+| 0.0049 | 100  | 0.2933        | 0.2839          |
+| 0.0074 | 150  | 0.262         | -               |
+| 0.0098 | 200  | 0.2523        | 0.2480          |
+| 0.0123 | 250  | 0.2403        | -               |
+| 0.0147 | 300  | 0.2185        | 0.2199          |
+| 0.0172 | 350  | 0.1983        | -               |
+| 0.0196 | 400  | 0.1874        | 0.2003          |
+| 0.0221 | 450  | 0.1727        | -               |
+| 0.0245 | 500  | 0.1568        | 0.1882          |
+| 0.0270 | 550  | 0.1386        | -               |
+| 0.0294 | 600  | 0.1181        | 0.1742          |
+| 0.0319 | 650  | 0.1023        | -               |
+| 0.0343 | 700  | 0.0877        | 0.1766          |
+| 0.0368 | 750  | 0.0717        | -               |
+| 0.0392 | 800  | 0.0555        | 0.1854          |
+| 0.0417 | 850  | 0.0447        | -               |
+| 0.0441 | 900  | 0.0343        | 0.1841          |
+
+### 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-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": "4.42.1",
+  "vocab_size": 30527
+}

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": 3,
+  "spacy_model": "en_core_web_sm",
+  "normalize_embeddings": false,
+  "labels": [
+    "negative",
+    "neutral",
+    "positive"
+  ]
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:104e4bf6fe2fc6277b2c19b3ba231f152aea5578a157f5496f6a56e108829955
+size 437967672

model_head.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82a79584241ba6917aba72ba12af73a373d4af2bea60ea13c100c93e37d6dd91
+size 19375

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": 384,
+  "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "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": {
+      "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": 384,
+  "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]"
+}