Update README.md

README.md

@@ -46,7 +46,7 @@ tags:
 
 在进行爬取，清洗，处理后得到510w对文本对（还在持续增加），batchzise=80训练了20个epoch，使st的权重能够适应该问题空间，生成融合了领域知识的文本特征向量（体现为有关的文本距离更加接近，例如作品与登场人物，或者来自同一作品的登场人物）。
 
-## Usage (Sentence-Transformers)
+## Usage
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
@@ -66,54 +66,6 @@ print(embeddings)
 ```
 
 
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
-
-
-## Training
-
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`torch.utils.data.dataloader.DataLoader` of length 64769 with parameters:
-
-```
-{'batch_size': 80, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
-
-  ```
-{'scale': 20.0, 'similarity_fct': 'cos_sim'}
-  ```
-
-Parameters of the fit()-Method:
-
-```
-{
-    "epochs": 20,
-    "evaluation_steps": 0,
-    "evaluator": "NoneType",
-    "max_grad_norm": 1,
-    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
-    "optimizer_params": {
-        "lr": 2e-05
-    },
-    "scheduler": "WarmupLinear",
-    "steps_per_epoch": null,
-    "warmup_steps": 129538,
-    "weight_decay": 0.01
-}
-```
-
-
 ## Full Model Architecture
 
 ```