Add SetFit model

1_Pooling/config.json

@@ -1,5 +1,5 @@
 {
-  "word_embedding_dimension": 384,
+  "word_embedding_dimension": 768,
   "pooling_mode_cls_token": false,
   "pooling_mode_mean_tokens": true,
   "pooling_mode_max_tokens": false,

2_Dense/config.json

@@ -0,0 +1 @@
+{"in_features": 768, "out_features": 512, "bias": true, "activation_function": "torch.nn.modules.activation.Tanh"}

2_Dense/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:075d536dea66f90ccb766d9d3ed550f94b9331872d9e564a8cd65159ff61ddbf
+size 1575072

README.md

@@ -5,10 +5,14 @@ tags:
 - sentence-transformers
 - text-classification
 - generated_from_setfit_trainer
-base_model: sentence-transformers/all-MiniLM-L6-v2
+base_model: sentence-transformers/distiluse-base-multilingual-cased-v2
 metrics:
 - accuracy
 widget:
+- text: explain in detail what is FFT and the complexity of it
+- text: The p success of karger min cut after k steps
+- text: Giải thích sự khác biệt giữa mô hình học có giám sát và không giám sát. Cung
+    cấp ví dụ cho từng loại. (ít nhất 150 từ)
 - text: 'Which of the following is a Code-Based Test Coverage Metrics(E. F. Miller,
     1977 dissertation)?
 
@@ -33,14 +37,11 @@ widget:
     d.
 
     C2: C0 coverage + loop coverage'
-- text: Phần mềm kiểm thử là gì?
-- text: Giải thích sự khác biệt giữa kiểm thử hộp đen và kiểm thử hộp trắng. Cung
-    cấp ví dụ cho từng loại. (ít nhất 150 từ)
-- text: Thủ đô của nước Pháp là gì?
+- text: What is software testing?
 pipeline_tag: text-classification
 inference: true
 model-index:
-- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
+- name: SetFit with sentence-transformers/distiluse-base-multilingual-cased-v2
   results:
   - task:
       type: text-classification
@@ -55,9 +56,9 @@ model-index:
       name: Accuracy
 ---
 
-# SetFit with sentence-transformers/all-MiniLM-L6-v2
+# SetFit with sentence-transformers/distiluse-base-multilingual-cased-v2
 
-This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. 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.
+This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-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.
 
 The model has been trained using an efficient few-shot learning technique that involves:
 
@@ -68,9 +69,9 @@ The model has been trained using an efficient few-shot learning technique that i
 
 ### Model Description
 - **Model Type:** SetFit
-- **Sentence Transformer body:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
+- **Sentence Transformer body:** [sentence-transformers/distiluse-base-multilingual-cased-v2](https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v2)
 - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
-- **Maximum Sequence Length:** 256 tokens
+- **Maximum Sequence Length:** 128 tokens
 - **Number of Classes:** 2 classes
 <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
 <!-- - **Language:** Unknown -->
@@ -83,10 +84,10 @@ The model has been trained using an efficient few-shot learning technique that i
 - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
 
 ### Model Labels
-| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                           |
-|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| 1     | <ul><li>'Giải thích sự khác biệt giữa mô hình học có giám sát và không giám sát. Cung cấp ví dụ cho từng loại. (ít nhất 150 từ)'</li><li>'Analyze the time complexity of the merge sort algorithm.'</li><li>'Xác suất để trúng giải thưởng khi bạn mua một tờ vé số là 0.05%. Giả sử mỗi ngày bạn mua 1 tờ vé số, vậy\nchúng ta cần bao nhiêu ngày (trung bình) để có 98% cơ hội trúng?'</li></ul> |
-| 0     | <ul><li>'Nêu ngắn gọn về quá trình quang hợp.'</li><li>'Viết một hàm Python tính giai thừa của một số.'</li><li>'Briefly describe the concept of photosynthesis.'</li></ul>                                                                                                                                                                                                                        |
+| Label | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+|:------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| 0     | <ul><li>'What is the capital of France?'</li><li>'Nêu ngắn gọn về quá trình quang hợp.'</li><li>'Briefly describe the concept of photosynthesis.'</li></ul>                                                                                                                                                                                                                                                                                                                                                                                              |
+| 1     | <ul><li>'What is White-box testing?\nCâu hỏi 7Trả lời\n\na.\nAll of the other answers.\n\nb.\nA testing technique in which internal structure, design and coding of software are tested.\n\nc.\nIts foundation is to execute every part of the code at least once.\n\nd.\nIn this technique, code is visible to testers.'</li><li>'For the expression "(a AND (b OR c))", which of the following test-cases is Multiple Condition Coverage (MCC)?'</li><li>'The p success of karger min cut after k steps. Tell me the detail of the equation'</li></ul> |
 
 ## Evaluation
 
@@ -113,7 +114,7 @@ from setfit import SetFitModel
 # Download from the 🤗 Hub
 model = SetFitModel.from_pretrained("chibao24/model_routing_few_shot")
 # Run inference
-preds = model("Phần mềm kiểm thử là gì?")
+preds = model("What is software testing?")
 ```
 
 <!--
@@ -145,12 +146,12 @@ preds = model("Phần mềm kiểm thử là gì?")
 ### Training Set Metrics
 | Training set | Min | Median  | Max |
 |:-------------|:----|:--------|:----|
-| Word count   | 4   | 24.7619 | 115 |
+| Word count   | 3   | 20.2903 | 115 |
 
 | Label | Training Sample Count |
 |:------|:----------------------|
-| 0     | 10                    |
-| 1     | 11                    |
+| 0     | 16                    |
+| 1     | 15                    |
 
 ### Training Hyperparameters
 - batch_size: (4, 4)
@@ -172,15 +173,21 @@ preds = model("Phần mềm kiểm thử là gì?")
 ### Training Results
 | Epoch   | Step    | Training Loss | Validation Loss |
 |:-------:|:-------:|:-------------:|:---------------:|
-| 0.0164  | 1       | 0.1956        | -               |
-| 0.8197  | 50      | 0.1926        | -               |
-| 1.0     | 61      | -             | 0.1463          |
-| 1.6393  | 100     | 0.0228        | -               |
-| **2.0** | **122** | **-**         | **0.0374**      |
-| 2.4590  | 150     | 0.017         | -               |
-| 3.0     | 183     | -             | 0.0507          |
-| 3.2787  | 200     | 0.003         | -               |
-| 4.0     | 244     | -             | 0.0443          |
+| 0.0078  | 1       | 0.4319        | -               |
+| 0.3906  | 50      | 0.1028        | -               |
+| 0.7812  | 100     | 0.0021        | -               |
+| **1.0** | **128** | **-**         | **0.2328**      |
+| 1.1719  | 150     | 0.0002        | -               |
+| 1.5625  | 200     | 0.0           | -               |
+| 1.9531  | 250     | 0.0001        | -               |
+| 2.0     | 256     | -             | 0.3315          |
+| 2.3438  | 300     | 0.0           | -               |
+| 2.7344  | 350     | 0.0002        | -               |
+| 3.0     | 384     | -             | 0.2364          |
+| 3.125   | 400     | 0.0001        | -               |
+| 3.5156  | 450     | 0.0           | -               |
+| 3.9062  | 500     | 0.0001        | -               |
+| 4.0     | 512     | -             | 0.3333          |
 
 * The bold row denotes the saved checkpoint.
 ### Framework Versions

config.json

@@ -1,26 +1,26 @@
 {
-  "_name_or_path": "checkpoints/step_122",
+  "_name_or_path": "checkpoints/step_128",
+  "activation": "gelu",
   "architectures": [
-    "BertModel"
+    "DistilBertModel"
   ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": null,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 384,
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
   "initializer_range": 0.02,
-  "intermediate_size": 1536,
-  "layer_norm_eps": 1e-12,
   "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 6,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "output_hidden_states": true,
+  "output_past": true,
   "pad_token_id": 0,
-  "position_embedding_type": "absolute",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
   "torch_dtype": "float32",
   "transformers_version": "4.41.2",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
+  "vocab_size": 119547
 }

config_setfit.json

@@ -1,7 +1,7 @@
 {
+  "normalize_embeddings": false,
   "labels": [
     0,
     1
-  ],
-  "normalize_embeddings": false
+  ]
 }

model.safetensors

@@ -1,3 +1,3 @@
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-size 90864192
+oid sha256:699c83648fa8570f6733bd6022605955a6fd5cb9f189f6b21d7e076466ea8357
+size 538947416

model_head.pkl

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+size 4959

modules.json

@@ -14,7 +14,7 @@
   {
     "idx": 2,
     "name": "2",
-    "path": "2_Normalize",
-    "type": "sentence_transformers.models.Normalize"
+    "path": "2_Dense",
+    "type": "sentence_transformers.models.Dense"
   }
 ]

sentence_bert_config.json

@@ -1,4 +1,4 @@
 {
-  "max_seq_length": 256,
+  "max_seq_length": 128,
   "do_lower_case": false
 }

tokenizer_config.json

@@ -44,10 +44,12 @@
   "clean_up_tokenization_spaces": true,
   "cls_token": "[CLS]",
   "do_basic_tokenize": true,
-  "do_lower_case": true,
+  "do_lower_case": false,
+  "full_tokenizer_file": null,
   "mask_token": "[MASK]",
+  "max_len": 512,
   "max_length": 128,
-  "model_max_length": 256,
+  "model_max_length": 128,
   "never_split": null,
   "pad_to_multiple_of": null,
   "pad_token": "[PAD]",
@@ -57,7 +59,7 @@
   "stride": 0,
   "strip_accents": null,
   "tokenize_chinese_chars": true,
-  "tokenizer_class": "BertTokenizer",
+  "tokenizer_class": "DistilBertTokenizer",
   "truncation_side": "right",
   "truncation_strategy": "longest_first",
   "unk_token": "[UNK]"