Add SetFit model

README.md

@@ -5,40 +5,38 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
-- semantic-search
-- chinese
----
 
-# DMetaSoul/sbert-chinese-general-v2
+---
 
-此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在百万级语义相似数据集 [SimCLUE](https://github.com/CLUEbenchmark/SimCLUE) 上进行训练，适用于**通用语义匹配**场景，从效果来看该模型在各种任务上**泛化能力更好**。
+# {MODEL_NAME}
 
-注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-general-v2-distill)，也已经开源啦！
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-# Usage
+<!--- Describe your model here -->
 
-## 1. Sentence-Transformers
+## Usage (Sentence-Transformers)
 
-通过  [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装：
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
 ```
 pip install -U sentence-transformers
 ```
 
-然后使用下面的代码来载入该模型并进行文本表征向量的提取：
+Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
+sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('DMetaSoul/sbert-chinese-general-v2')
+model = SentenceTransformer('{MODEL_NAME}')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-## 2. HuggingFace Transformers
 
-如果不想使用   [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取：
+
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
 ```python
 from transformers import AutoTokenizer, AutoModel
@@ -53,11 +51,11 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"]
+sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-general-v2')
-model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v2')
+tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
+model = AutoModel.from_pretrained('{MODEL_NAME}')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -73,17 +71,56 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
-## Evaluation
 
-该模型在公开的几个语义匹配数据集上进行了评测，计算了向量相似度跟真实标签之间的相关性系数：
 
-|                              | **csts_dev** | **csts_test** | **afqmc**  | **lcqmc**  | **bqcorpus** | **pawsx**  | **xiaobu** |
-| ---------------------------- | ------------ | ------------- | ---------- | ---------- | ------------ | ---------- | ---------- |
-| **sbert-chinese-general-v1** | **84.54%**   | **82.17%**    | 23.80%     | 65.94%     | 45.52%       | 11.52%     | 48.51%     |
-| **sbert-chinese-general-v2** | 77.20%       | 72.60%        | **36.80%** | **76.92%** | **49.63%**   | **16.24%** | **63.16%** |
+## 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})
 
-这里对比了本模型跟之前我们发布 [sbert-chinese-general-v1](https://huggingface.co/DMetaSoul/sbert-chinese-general-v1) 之间的差异，可以看到本模型在多个任务上的泛化能力更好。
+
+## Training
+The model was trained with the parameters:
+
+**DataLoader**:
+
+`torch.utils.data.dataloader.DataLoader` of length 765 with parameters:
+```
+{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
+
+**Loss**:
+
+`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` 
+
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 1,
+    "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": 765,
+    "warmup_steps": 77,
+    "weight_decay": 0.01
+}
+```
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+)
+```
 
 ## Citing & Authors
 
-E-mail: xiaowenbin@dmetasoul.com
+<!--- Describe where people can find more information -->

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