Add SetFit ABSA model

README.md

@@ -8,37 +8,10 @@ tags:
 - generated_from_setfit_trainer
 metrics:
 - accuracy
-widget:
-- text: waiter:After sitting at the table with empty glasses for a 1/2 hour, we had
-    to ask the busboys to get us drinks as our waiter was nowhere to be found.
-- text: presentation:The service was impeccible, the menu traditional but inventive
-    and presentation for the mostpart excellent but the food itself came up short.
-- text: Friday night:Without reservations on a Friday night at 8:30 I was promptly
-    seated and given top-notch recommendations from both the host and my waiter.
-- text: time:last time, the waiter told my roommate he'd have to charge her $5 for
-    mushrooms as one of her omelette choices (never heard that at my other favorite
-    brunch places.
-- text: waitstaff: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 Aspect 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: 0.8051948051948052
-      name: Accuracy
 ---
 
 # SetFit Aspect Model with sentence-transformers/paraphrase-mpnet-base-v2
@@ -77,19 +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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
-|:----------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| aspect    | <ul><li>'decor:The decor is not special at all but their food and amazing prices make up for it.'</li><li>'food:The decor is not special at all but their food and amazing prices make up for it.'</li><li>'prices:The decor is not special at all but their food and amazing prices make up for it.'</li></ul>                                                                                                                                                                                      |
-| no aspect | <ul><li>'party:when tables opened up, the manager sat another party before us.'</li><li>"offerings: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>"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> |
-
-## Evaluation
-
-### Metrics
-| Label   | Accuracy |
-|:--------|:---------|
-| **all** | 0.8052   |
-
 ## Uses
 
 ### Direct Use for Inference
@@ -140,75 +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   | 7   | 29.7429 | 63  |
-
-| Label     | Training Sample Count |
-|:----------|:----------------------|
-| no aspect | 115                   |
-| aspect    | 130                   |
-
-### 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 |
-|:------:|:----:|:-------------:|:---------------:|
-| 0.0005 | 1    | 0.2136        | -               |
-| 0.0263 | 50   | 0.264         | -               |
-| 0.0527 | 100  | 0.2717        | -               |
-| 0.0790 | 150  | 0.2099        | -               |
-| 0.1053 | 200  | 0.1357        | -               |
-| 0.1316 | 250  | 0.1224        | -               |
-| 0.1580 | 300  | 0.0305        | -               |
-| 0.1843 | 350  | 0.0016        | -               |
-| 0.2106 | 400  | 0.0015        | -               |
-| 0.2370 | 450  | 0.0004        | -               |
-| 0.2633 | 500  | 0.0006        | -               |
-| 0.2896 | 550  | 0.0109        | -               |
-| 0.3160 | 600  | 0.0002        | -               |
-| 0.3423 | 650  | 0.0001        | -               |
-| 0.3686 | 700  | 0.0001        | -               |
-| 0.3949 | 750  | 0.0003        | -               |
-| 0.4213 | 800  | 0.0001        | -               |
-| 0.4476 | 850  | 0.0002        | -               |
-| 0.4739 | 900  | 0.0001        | -               |
-| 0.5003 | 950  | 0.0002        | -               |
-| 0.5266 | 1000 | 0.0001        | -               |
-| 0.5529 | 1050 | 0.0001        | -               |
-| 0.5793 | 1100 | 0.0001        | -               |
-| 0.6056 | 1150 | 0.0001        | -               |
-| 0.6319 | 1200 | 0.0002        | -               |
-| 0.6582 | 1250 | 0.0001        | -               |
-| 0.6846 | 1300 | 0.0001        | -               |
-| 0.7109 | 1350 | 0.0001        | -               |
-| 0.7372 | 1400 | 0.0001        | -               |
-| 0.7636 | 1450 | 0.0001        | -               |
-| 0.7899 | 1500 | 0.0001        | -               |
-| 0.8162 | 1550 | 0.0001        | -               |
-| 0.8425 | 1600 | 0.0169        | -               |
-| 0.8689 | 1650 | 0.0001        | -               |
-| 0.8952 | 1700 | 0.0001        | -               |
-| 0.9215 | 1750 | 0.0001        | -               |
-| 0.9479 | 1800 | 0.0001        | -               |
-| 0.9742 | 1850 | 0.0001        | -               |
-
 ### 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-aspect",
   "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]"
 }