完善ModelCard

README.md

@@ -1,62 +1,160 @@
----
+这部分是YAML元数据，Hugging Face会用它来分类和展示你的模型
 library_name: transformers
 license: apache-2.0
 base_model: distilbert-base-uncased
 tags:
-- generated_from_trainer
+
+sentiment-analysis # 任务标签
+
+text-classification # 任务标签
+
+imdb # 数据集标签
+
+generated_from_trainer # 表明是使用Trainer训练的
 metrics:
-- accuracy
+
+accuracy
 model-index:
-- name: imdb-sentiment-distilbert
-  results: []
----
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+name: imdb-sentiment-distilbert
+results:
+
+task:
+type: text-classification
+dataset:
+name: imdb
+type: imdb
+metrics:
+
+name: Accuracy
+type: accuracy
+value: 0.85
+
+情感分析模型：distilbert-base-uncased-imdb
+这是一个基于 distilbert-base-uncased 模型，在经典的 IMDB 电影评论数据集 上进行微调的情感分析模型。它能够高效地判断一段英文文本所表达的情感是正面的还是负面的。
+
+🚀 模型性能
+该模型在 IMDB 数据集的评估集上取得了以下性能：
+
+指标 (Metric)
+
+数值 (Value)
+
+评估损失 (Loss)
+
+0.3455
+
+准确率 (Accuracy)
+
+0.85
+
+💡 如何使用
+您可以非常方便地使用 transformers 库中的 pipeline 来调用这个模型。
+
+# 安装transformers库
+# pip install transformers
+
+from transformers import pipeline
+
+# 使用您的模型仓库ID加载pipeline
+# 请将 "YOUR_USERNAME/YOUR_REPO_NAME" 替换为您的模型地址
+sentiment_pipeline = pipeline(
+    "sentiment-analysis", 
+    model="YOUR_USERNAME/imdb-sentiment-distilbert"
+)
+
+# 测试正面评论
+positive_comment = "This movie was absolutely fantastic, a masterpiece of modern cinema!"
+result_pos = sentiment_pipeline(positive_comment)
+print(f"评论: '{positive_comment}'")
+print(f"情感分析结果: {result_pos}")
+# 预期输出: [{'label': 'POSITIVE', 'score': ...}]
+
+print("-" * 50)
+
+# 测试负面评论
+negative_comment = "I would not recommend this film, it was quite boring and a waste of time."
+result_neg = sentiment_pipeline(negative_comment)
+print(f"评论: '{negative_comment}'")
+print(f"情感分析结果: {result_neg}")
+# 预期输出: [{'label': 'NEGATIVE', 'score': ...}]
+
+📚 训练细节
+训练数据
+本模型使用了 imdb 数据集进行训练和评估。该数据集包含 50,000 条电影评论，其中 25,000 条用于训练，25,000 条用于测试。每条评论都被标记为 正面 (POSITIVE) 或 负面 (NEGATIVE)。为了快速完成项目，本次训练使用了其中的一小部分样本（1000条训练，1000条评估）。
+
+训练过程
+模型微调是基于 Hugging Face transformers 库的 Trainer API 完成的。
+
+超参数 (Hyperparameters)
+超参数
+
+值
+
+learning_rate
+
+2e-05
+
+train_batch_size
+
+16
+
+eval_batch_size
+
+16
+
+seed
+
+42
+
+optimizer
+
+AdamW (betas=(0.9,0.999), epsilon=1e-08)
+
+lr_scheduler_type
+
+linear
+
+num_epochs
+
+2
+
+训练结果日志
+Training Loss
+
+Epoch
+
+Step
 
-# imdb-sentiment-distilbert
+Validation Loss
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.3455
-- Accuracy: 0.85
+Accuracy
 
-## Model description
+No log
 
-More information needed
+1.0
 
-## Intended uses & limitations
+63
 
-More information needed
+0.4222
 
-## Training and evaluation data
+0.844
 
-More information needed
+No log
 
-## Training procedure
+2.0
 
-### Training hyperparameters
+126
 
-The following hyperparameters were used during training:
-- learning_rate: 2e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 2
+0.3455
 
-### Training results
+0.85
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| No log        | 1.0   | 63   | 0.4222          | 0.844    |
-| No log        | 2.0   | 126  | 0.3455          | 0.85     |
+框架版本
+Transformers: 4.56.0
 
+Pytorch: 2.8.0+cu126
 
-### Framework versions
+Datasets: 4.0.0
 
-- Transformers 4.56.0
-- Pytorch 2.8.0+cu126
-- Datasets 4.0.0
-- Tokenizers 0.22.0
+Tokenizers: 0.22.0