Add SetFit ABSA model

README.md

@@ -6,17 +6,19 @@ tags:
 - text-classification
 - generated_from_setfit_trainer
 widget:
-- text: food:What is even better, is that the prices are very affordable as well,
-    and the food is really good.
-- text: 'soups:An oasis of refinement:  Food, though somewhat uneven, often reaches
-    the pinnacles of new American fine cuisine - chef''s passion (and kitchen''s precise
-    execution) is most evident in the fish dishes and soups.'
-- text: lobster sandwich:We had the lobster sandwich and it was FANTASTIC.
-- text: back waiters:I understand the area and folks you need not come here for the
-    romantic, alluring ambiance or the five star service featuring a sommlier and
-    a complicated maze of captain and back waiters - you come for the authentic foods,
-    the tastes, the experiance.
-- text: dining experience:The entire dining experience was wonderful!
+- text: '- they use fresh mozzarella instead of the:The pizza is delicious - they
+    use fresh mozzarella instead of the cheap, frozen, shredded cheese common to most
+    pizzaria''s.'
+- text: 'refinement:  Food, though somewhat:An oasis of refinement:  Food, though
+    somewhat uneven, often reaches the pinnacles of new American fine cuisine - chef''s
+    passion (and kitchen''s precise execution) is most evident in the fish dishes
+    and soups.'
+- text: We had the lobster sandwich and it was:We had the lobster sandwich and it
+    was FANTASTIC.
+- text: The fish is fresh but:The fish is fresh but the variety of fish is nothing
+    out of ordinary.
+- text: with classic upscale Italian decor.:Nice restaurant overall, with classic
+    upscale Italian decor.
 metrics:
 - accuracy
 pipeline_tag: text-classification
@@ -24,7 +26,7 @@ library_name: setfit
 inference: false
 base_model: sentence-transformers/all-MiniLM-L6-v2
 model-index:
-- name: SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
+- name: SetFit Polarity Model with sentence-transformers/all-MiniLM-L6-v2
   results:
   - task:
       type: text-classification
@@ -35,13 +37,13 @@ model-index:
       split: test
     metrics:
     - type: accuracy
-      value: 0.8407079646017699
+      value: 0.7244094488188977
       name: Accuracy
 ---
 
-# SetFit Aspect Model with sentence-transformers/all-MiniLM-L6-v2
+# SetFit Polarity 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.
+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 classifying aspect polarities.
 
 The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -51,8 +53,8 @@ The model has been trained using an efficient few-shot learning technique that i
 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.
+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
 
@@ -61,10 +63,10 @@ This model was trained within the context of a larger system for ABSA, which loo
 - **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_lg
-- **SetFitABSA Aspect Model:** [ronalhung/setfit-absa-restaurants-polarity](https://huggingface.co/ronalhung/setfit-absa-restaurants-polarity)
-- **SetFitABSA Polarity Model:** [setfit-absa-polarity](https://huggingface.co/setfit-absa-polarity)
+- **SetFitABSA Aspect Model:** [ronalhung/setfit-absa-restaurants-aspect](https://huggingface.co/ronalhung/setfit-absa-restaurants-aspect)
+- **SetFitABSA Polarity Model:** [ronalhung/setfit-absa-restaurants-polarity](https://huggingface.co/ronalhung/setfit-absa-restaurants-polarity)
 - **Maximum Sequence Length:** 256 tokens
-- **Number of Classes:** 2 classes
+- **Number of Classes:** 4 classes
 <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
 <!-- - **Language:** Unknown -->
 <!-- - **License:** Unknown -->
@@ -76,17 +78,19 @@ This model was trained within the context of a larger system for ABSA, which loo
 - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
 
 ### Model Labels
-| Label     | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
-|:----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| aspect    | <ul><li>'staff:But the staff was so horrible to us.'</li><li>"food:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"food:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li></ul>                                                                                                                              |
-| no aspect | <ul><li>"factor:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"deficiencies:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"Teodora:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li></ul> |
+| Label    | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
+|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| negative | <ul><li>'But the staff was so horrible:But the staff was so horrible to us.'</li><li>', forgot our toast, left out:They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.'</li><li>'did not have mayonnaise, forgot our:They did not have mayonnaise, forgot our toast, left out ingredients (ie cheese in an omelet), below hot temperatures and the bacon was so over cooked it crumbled on the plate when you touched it.'</li></ul>                                          |
+| positive | <ul><li>"factor was the food, which was:To be completely fair, the only redeeming factor was the food, which was above average, but couldn't make up for all the other deficiencies of Teodora."</li><li>"The food is uniformly exceptional:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li><li>"a very capable kitchen which will proudly:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li></ul> |
+| neutral  | <ul><li>"'s on the menu or not.:The food is uniformly exceptional, with a very capable kitchen which will proudly whip up whatever you feel like eating, whether it's on the menu or not."</li><li>'to sample both meats).:Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).'</li><li>'to split the dish in half so:Our agreed favorite is the orrechiete with sausage and chicken (usually the waiters are kind enough to split the dish in half so you get to sample both meats).'</li></ul>                   |
+| conflict | <ul><li>'The food was delicious but:The food was delicious but do not come here on a empty stomach.'</li><li>"The service varys from day:The service varys from day to day- sometimes they're very nice, and sometimes not."</li></ul>                                                                                                                                                                                                                                                                                                                                                                                         |
 
 ## Evaluation
 
 ### Metrics
 | Label   | Accuracy |
 |:--------|:---------|
-| **all** | 0.8407   |
+| **all** | 0.7244   |
 
 ## Uses
 
@@ -105,8 +109,8 @@ from setfit import AbsaModel
 
 # Download from the 🤗 Hub
 model = AbsaModel.from_pretrained(
+    "ronalhung/setfit-absa-restaurants-aspect",
     "ronalhung/setfit-absa-restaurants-polarity",
-    "setfit-absa-polarity",
 )
 # Run inference
 preds = model("The food was great, but the venue is just way too busy.")
@@ -141,12 +145,14 @@ preds = model("The food was great, but the venue is just way too busy.")
 ### Training Set Metrics
 | Training set | Min | Median  | Max |
 |:-------------|:----|:--------|:----|
-| Word count   | 4   | 17.9296 | 37  |
+| Word count   | 6   | 21.3594 | 43  |
 
-| Label     | Training Sample Count |
-|:----------|:----------------------|
-| no aspect | 71                    |
-| aspect    | 128                   |
+| Label    | Training Sample Count |
+|:---------|:----------------------|
+| conflict | 2                     |
+| negative | 19                    |
+| neutral  | 25                    |
+| positive | 82                    |
 
 ### Training Hyperparameters
 - batch_size: (128, 128)
@@ -169,27 +175,23 @@ preds = model("The food was great, but the venue is just way too busy.")
 ### Training Results
 | Epoch  | Step | Training Loss | Validation Loss |
 |:------:|:----:|:-------------:|:---------------:|
-| 0.0015 | 1    | 0.3852        | -               |
-| 0.0740 | 50   | 0.2999        | 0.2943          |
-| 0.0059 | 1    | 0.2573        | -               |
-| 0.2959 | 50   | 0.2516        | 0.2540          |
-| 0.5917 | 100  | 0.1633        | 0.2385          |
-| 0.8876 | 150  | 0.015         | 0.2334          |
-| 1.1834 | 200  | 0.0025        | 0.2396          |
-| 1.4793 | 250  | 0.0013        | 0.2452          |
-| 1.7751 | 300  | 0.0009        | 0.2491          |
-| 2.0710 | 350  | 0.0007        | 0.2449          |
-| 2.3669 | 400  | 0.0005        | 0.2372          |
+| 0.0147 | 1    | 0.2928        | -               |
+| 0.7353 | 50   | 0.1985        | 0.2185          |
+| 1.4706 | 100  | 0.0538        | 0.2563          |
+| 2.2059 | 150  | 0.0219        | 0.2624          |
+| 2.9412 | 200  | 0.0101        | 0.2634          |
+| 3.6765 | 250  | 0.0046        | 0.2726          |
+| 4.4118 | 300  | 0.0033        | 0.2721          |
 
 ### Framework Versions
-- Python: 3.11.11
+- Python: 3.11.12
 - SetFit: 1.1.2
 - Sentence Transformers: 3.4.1
-- spaCy: 3.7.5
-- Transformers: 4.48.3
-- PyTorch: 2.5.1+cu124
+- spaCy: 3.8.5
+- Transformers: 4.51.3
+- PyTorch: 2.6.0+cu124
 - Datasets: 3.5.0
-- Tokenizers: 0.21.0
+- Tokenizers: 0.21.1
 
 ## Citation
 

config.json

@@ -1,5 +1,4 @@
 {
-  "_name_or_path": "sentence-transformers/all-MiniLM-L6-v2",
   "architectures": [
     "BertModel"
   ],
@@ -19,7 +18,7 @@
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.48.3",
+  "transformers_version": "4.51.3",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

config_sentence_transformers.json

@@ -1,8 +1,8 @@
 {
   "__version__": {
     "sentence_transformers": "3.4.1",
-    "transformers": "4.48.3",
-    "pytorch": "2.5.1+cu124"
+    "transformers": "4.51.3",
+    "pytorch": "2.6.0+cu124"
   },
   "prompts": {},
   "default_prompt_name": null,

config_setfit.json

@@ -1,9 +1,11 @@
 {
+  "span_context": 3,
   "labels": [
-    "no aspect",
-    "aspect"
+    "conflict",
+    "negative",
+    "neutral",
+    "positive"
   ],
   "spacy_model": "en_core_web_lg",
-  "normalize_embeddings": false,
-  "span_context": 0
+  "normalize_embeddings": false
 }

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:c29cedb2dd1612b46ef46356912347fec1d7be96d95cfad3ce2514aedfdbcbc9
 size 90864192

model_head.pkl

@@ -1,3 +1,3 @@
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-oid sha256:bbed4aa053d3a68620daf4411fe1596e6deee17a02f31cb5da5ef55ec0a34a8a
-size 3919
+oid sha256:4cfd7d3053ab9623988c3b9b73e34f52fdfcd8cdd4a977b36af33acbf795dac4
+size 13287