hiroki-rad
/

bert-base-classification-ft

@@ -20,6 +20,17 @@ pipeline_tag: text-classification
 ## Model Details
 ### Model Description
@@ -35,31 +46,20 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 - **License:** [More Information Needed]
 - **Finetuned from model [optional]:** [cl-tohoku/bert-base-japanese-v3]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
 from transformers import pipeline
-このlabel2idで学習しました。label2idはこれを利用してください。
-label2id = {'Task_Solution': 0,
-             'Creative_Generation': 1,
-             'Knowledge_Explanation': 2,
-             'Analytical_Reasoning': 3,
-             'Information_Extraction': 4,
-             'Step_by_Step_Calculation': 5,
-             'Role_Play_Response': 6,
-             'Opinion_Perspective': 7}
 def preprocess_text_classification(examples: dict[str, list]) -> BatchEncoding:
     """バッチ処理用に修正"""
@@ -75,7 +75,7 @@ def preprocess_text_classification(examples: dict[str, list]) -> BatchEncoding:
     encoded_examples["labels"] = [label2id[label] for label in examples["labels"]]
     return encoded_examples
-##使用するデータセット
 test_data = test_data.to_pandas()
 test_data["labels"] = test_data["labels"].apply(lambda x: label2id[x])
 test_data
@@ -88,13 +88,11 @@ label2id = {label: id for id, label in enumerate(class_label)}
 id2label = {id: label for id, label in enumerate(class_label)}
 results: list[dict[str, float | str]] = []
 for i, example in tqdm(enumerate(test_data.itertuples())):
     # モデルの予測結果を取得
     model_prediction = classify_pipe(example.questions)[0]
     # 正解のラベルIDをラベル名に変換
     true_label = id2label[example.labels]
     results.append(
         {
             "example_id": i,
@@ -103,158 +101,4 @@ for i, example in tqdm(enumerate(test_data.itertuples())):
             "true_label": true_label,
         }
     )
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!https://huggingface.co/datasets/elyza/ELYZA-tasks-100>
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ## Model Details
+elyzaタスク100のタスクのinputを入力してタスクを分類するためのタスクです。
+タスクの分類は以下のものです。
+- 知識説明型	Knowledge Explanation
+- 創作型	Creative Generation
+- 分析推論型	Analytical Reasoning
+- 課題解決型	Task Solution
+- 情報抽出型	Information Extraction
+- 計算・手順型	Step-by-Step Calculation
+- 意見・視点型	Opinion-Perspective
+- ロールプレイ型	Role-Play Response
 ### Model Description
 - **License:** [More Information Needed]
 - **Finetuned from model [optional]:** [cl-tohoku/bert-base-japanese-v3]
 ### Direct Use
+```python
 from transformers import pipeline
+label2id = {
+    'Task_Solution': 0,
+    'Creative_Generation': 1,
+    'Knowledge_Explanation': 2,
+    'Analytical_Reasoning': 3,
+    'Information_Extraction': 4,
+    'Step_by_Step_Calculation': 5,
+    'Role_Play_Response': 6,
+    'Opinion_Perspective': 7
+}
 def preprocess_text_classification(examples: dict[str, list]) -> BatchEncoding:
     """バッチ処理用に修正"""
     encoded_examples["labels"] = [label2id[label] for label in examples["labels"]]
     return encoded_examples
+# 使用するデータセット
 test_data = test_data.to_pandas()
 test_data["labels"] = test_data["labels"].apply(lambda x: label2id[x])
 test_data
 id2label = {id: label for id, label in enumerate(class_label)}
 results: list[dict[str, float | str]] = []
 for i, example in tqdm(enumerate(test_data.itertuples())):
     # モデルの予測結果を取得
     model_prediction = classify_pipe(example.questions)[0]
     # 正解のラベルIDをラベル名に変換
     true_label = id2label[example.labels]
     results.append(
         {
             "example_id": i,
             "true_label": true_label,
         }
     )
+```