Add SetFit ABSA model

README.md

@@ -8,38 +8,10 @@ tags:
 - generated_from_setfit_trainer
 metrics:
 - accuracy
-widget:
-- text: All of the drinks that we tried:All of the drinks that we tried were As for
-    desserts, my favorite is the chocolate cake and my boyfriend really liked their
-    pumpkin cheesecake.
-- text: knew where the lounge was since all:The hostess made sure we knew where the
-    lounge was since all the seats at the bar were full and had the waiter come over
-    to take our drink order.
-- text: sushi a big hamburger and good coctails:sushi a big hamburger and good coctails.
-- text: impeccible, the menu traditional but inventive:The service was impeccible,
-    the menu traditional but inventive and presentation for the mostpart excellent
-    but the food itself came up short.
-- text: food, they served me the wrong:And the waitstaff has very little knowledge
-    of the food, they served me the wrong dish and no one could identify what it was
-    that they gave me, someone said pork chop, someone said lamb, and then they insisted
-    it should be fine since it was the same price.
+widget: []
 pipeline_tag: text-classification
 inference: false
 base_model: sentence-transformers/paraphrase-mpnet-base-v2
-model-index:
-- name: SetFit Polarity Model with sentence-transformers/paraphrase-mpnet-base-v2
-  results:
-  - task:
-      type: text-classification
-      name: Text Classification
-    dataset:
-      name: Unknown
-      type: unknown
-      split: test
-    metrics:
-    - type: accuracy
-      value: 0.6201550387596899
-      name: Accuracy
 ---
 
 # SetFit Polarity Model with sentence-transformers/paraphrase-mpnet-base-v2
@@ -78,20 +50,6 @@ This model was trained within the context of a larger system for ABSA, which loo
 - **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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
-|:---------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| negative | <ul><li>'The decor is not special:The decor is not special at all but their food and amazing prices make up for it.'</li><li>'up, the manager sat another party:when tables opened up, the manager sat another party before us.'</li><li>"offerings (a peanut butter roll, for instance:Though the menu includes some unorthodox offerings (a peanut butter roll, for instance), the classics are pure and great--we've never had better sushi anywhere, including Japan."</li></ul>                               |
-| positive | <ul><li>'all but their food and amazing prices:The decor is not special at all but their food and amazing prices make up for it.'</li><li>'food and amazing prices make up for:The decor is not special at all but their food and amazing prices make up for it.'</li><li>"), the classics are pure and:Though the menu includes some unorthodox offerings (a peanut butter roll, for instance), the classics are pure and great--we've never had better sushi anywhere, including Japan."</li></ul>               |
-| neutral  | <ul><li>'when tables opened up,:when tables opened up, the manager sat another party before us.'</li><li>"Though the menu includes some unorthodox:Though the menu includes some unorthodox offerings (a peanut butter roll, for instance), the classics are pure and great--we've never had better sushi anywhere, including Japan."</li><li>'five mins if food was ok,:service is good although a bit in your face, we were asked every five mins if food was ok, but better that than being ignored.'</li></ul> |
-
-## Evaluation
-
-### Metrics
-| Label   | Accuracy |
-|:--------|:---------|
-| **all** | 0.6202   |
-
 ## Uses
 
 ### Direct Use for Inference
@@ -142,52 +100,6 @@ preds = model("The food was great, but the venue is just way too busy.")
 
 ## Training Details
 
-### Training Set Metrics
-| Training set | Min | Median  | Max |
-|:-------------|:----|:--------|:----|
-| Word count   | 10  | 32.1231 | 69  |
-
-| Label    | Training Sample Count |
-|:---------|:----------------------|
-| negative | 28                    |
-| neutral  | 66                    |
-| positive | 36                    |
-
-### Training Hyperparameters
-- batch_size: (16, 2)
-- num_epochs: (1, 16)
-- 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: 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 |
-|:------:|:----:|:-------------:|:---------------:|
-| 0.0015 | 1    | 0.2831        | -               |
-| 0.0765 | 50   | 0.2026        | -               |
-| 0.1529 | 100  | 0.2559        | -               |
-| 0.2294 | 150  | 0.1234        | -               |
-| 0.3058 | 200  | 0.0054        | -               |
-| 0.3823 | 250  | 0.002         | -               |
-| 0.4587 | 300  | 0.0005        | -               |
-| 0.5352 | 350  | 0.0003        | -               |
-| 0.6116 | 400  | 0.0003        | -               |
-| 0.6881 | 450  | 0.0003        | -               |
-| 0.7645 | 500  | 0.0002        | -               |
-| 0.8410 | 550  | 0.0003        | -               |
-| 0.9174 | 600  | 0.0003        | -               |
-| 0.9939 | 650  | 0.0002        | -               |
-
 ### Framework Versions
 - Python: 3.10.12
 - SetFit: 1.0.3

config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "sentence-transformers/paraphrase-mpnet-base-v2",
+  "_name_or_path": "NazmusAshrafi/atsa-mams-ds-setfit-MiniLM-mpnet-absa-tesla-tweet-polarity",
   "architectures": [
     "MPNetModel"
   ],

tokenizer_config.json

@@ -48,12 +48,19 @@
   "do_lower_case": true,
   "eos_token": "</s>",
   "mask_token": "<mask>",
+  "max_length": 512,
   "model_max_length": 512,
   "never_split": null,
+  "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]"
 }