Add SetFit ABSA model

README.md

@@ -6,19 +6,15 @@ tags:
 - text-classification
 - generated_from_setfit_trainer
 widget:
-- 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.
+- text: even the wine by the glass was good.:even the wine by the glass was good.
+- text: I had the Pad Thai and the noodles:I had the Pad Thai and the noodles were
+    sticky.
+- text: happy and the food was delicious,:The have over 100 different beers to offer
+    thier guest so that made my husband very happy and the food was delicious, if
+    I must recommend a dish it must be the pumkin tortelini.
+- text: The takeout menu says to keep:The takeout menu says to keep an eye out for
+    an expanded menu offering more italian dishes, I can't wait!
+- text: fresh garlic or eggplant.:Try their plain pizza with fresh garlic or eggplant.
 metrics:
 - accuracy
 - f1_micro
@@ -44,28 +40,28 @@ model-index:
       split: test
     metrics:
     - type: accuracy
-      value: 0.7244094488188977
+      value: 0.6956390066626287
       name: Accuracy
     - type: f1_micro
-      value: 0.7244094488188977
+      value: 0.6956390066626287
       name: F1_Micro
     - type: f1_macro
-      value: 0.34185676966338874
+      value: 0.4329017416100218
       name: F1_Macro
     - type: f1_weighted
-      value: 0.6451357884163048
+      value: 0.6632256738950839
       name: F1_Weighted
     - type: precision_micro
-      value: 0.7244094488188977
+      value: 0.6956390066626287
       name: Precision_Micro
     - type: precision_macro
-      value: 0.4194321533923304
+      value: 0.45635478257069084
       name: Precision_Macro
     - type: recall_micro
-      value: 0.7244094488188977
+      value: 0.6956390066626287
       name: Recall_Micro
     - type: recall_macro
-      value: 0.36837121212121215
+      value: 0.43398523441592457
       name: Recall_Macro
 ---
 
@@ -111,14 +107,14 @@ This model was trained within the context of a larger system for ABSA, which loo
 | 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>                                                                                                                                                                                                                                                                                                                                                                                         |
+| 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><li>'Though the Spider Roll may look like:Though the Spider Roll may look like a challenge to eat, with soft shell crab hanging out of the roll, it is well worth the price you pay for them.'</li></ul>                                                                                                                                                                                      |
 
 ## Evaluation
 
 ### Metrics
 | Label   | Accuracy | F1_Micro | F1_Macro | F1_Weighted | Precision_Micro | Precision_Macro | Recall_Micro | Recall_Macro |
 |:--------|:---------|:---------|:---------|:------------|:----------------|:----------------|:-------------|:-------------|
-| **all** | 0.7244   | 0.7244   | 0.3419   | 0.6451      | 0.7244          | 0.4194          | 0.7244       | 0.3684       |
+| **all** | 0.6956   | 0.6956   | 0.4329   | 0.6632      | 0.6956          | 0.4564          | 0.6956       | 0.4340       |
 
 ## Uses
 
@@ -173,14 +169,14 @@ preds = model("The food was great, but the venue is just way too busy.")
 ### Training Set Metrics
 | Training set | Min | Median  | Max |
 |:-------------|:----|:--------|:----|
-| Word count   | 6   | 21.3594 | 43  |
+| Word count   | 6   | 22.4961 | 51  |
 
 | Label    | Training Sample Count |
 |:---------|:----------------------|
-| conflict | 2                     |
-| negative | 19                    |
-| neutral  | 25                    |
-| positive | 82                    |
+| conflict | 6                     |
+| negative | 43                    |
+| neutral  | 36                    |
+| positive | 169                   |
 
 ### Training Hyperparameters
 - batch_size: (128, 128)
@@ -203,22 +199,23 @@ preds = model("The food was great, but the venue is just way too busy.")
 ### Training Results
 | Epoch  | Step | Training Loss | Validation Loss |
 |:------:|:----:|:-------------:|:---------------:|
-| 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          |
+| 0.0039 | 1    | 0.3102        | -               |
+| 0.1946 | 50   | 0.272         | 0.2236          |
+| 0.3891 | 100  | 0.1589        | 0.2166          |
+| 0.5837 | 150  | 0.0554        | 0.2199          |
+| 0.7782 | 200  | 0.0267        | 0.2241          |
+| 0.9728 | 250  | 0.0191        | 0.2276          |
+| 1.1673 | 300  | 0.0095        | 0.2284          |
+| 1.3619 | 350  | 0.0045        | 0.2320          |
 
 ### Framework Versions
-- Python: 3.11.12
+- Python: 3.11.13
 - SetFit: 1.1.2
-- Sentence Transformers: 3.4.1
-- spaCy: 3.8.5
-- Transformers: 4.51.3
+- Sentence Transformers: 4.1.0
+- spaCy: 3.8.7
+- Transformers: 4.52.4
 - PyTorch: 2.6.0+cu124
-- Datasets: 3.5.1
+- Datasets: 3.6.0
 - Tokenizers: 0.21.1
 
 ## Citation

config.json

@@ -18,7 +18,7 @@
   "pad_token_id": 0,
   "position_embedding_type": "absolute",
   "torch_dtype": "float32",
-  "transformers_version": "4.51.3",
+  "transformers_version": "4.52.4",
   "type_vocab_size": 2,
   "use_cache": true,
   "vocab_size": 30522

config_sentence_transformers.json

@@ -1,7 +1,7 @@
 {
   "__version__": {
-    "sentence_transformers": "3.4.1",
-    "transformers": "4.51.3",
+    "sentence_transformers": "4.1.0",
+    "transformers": "4.52.4",
     "pytorch": "2.6.0+cu124"
   },
   "prompts": {},

config_setfit.json

@@ -1,5 +1,4 @@
 {
-  "spacy_model": "en_core_web_lg",
   "normalize_embeddings": false,
   "labels": [
     "conflict",
@@ -7,5 +6,6 @@
     "neutral",
     "positive"
   ],
+  "spacy_model": "en_core_web_lg",
   "span_context": 3
 }

model.safetensors

@@ -1,3 +1,3 @@
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 size 90864192

model_head.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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+oid sha256:e1094f805f1b8676e0c432024f7143e780d71c3f72e2a10280f4817ec9ed7b10
 size 13287