add bce example

llm/inference/bce/run_bce-embedding.py

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+from mindspore import ops
+from mindnlp.transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification
+
+hf_token = 'your_hf_token'
+
+# list of sentences
+sentences = ['sentence_0', 'sentence_1']
+
+# init model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained('maidalun1020/bce-embedding-base_v1', token=hf_token)
+model = AutoModel.from_pretrained('maidalun1020/bce-embedding-base_v1', token=hf_token)
+
+# get inputs
+inputs = tokenizer(sentences, padding=True, truncation=True, max_length=512, return_tensors="ms")
+inputs = {k: v for k, v in inputs.items()}
+
+# get embeddings
+outputs = model(**inputs, return_dict=True)
+embeddings = outputs.last_hidden_state[:, 0]  # cls pooler
+embeddings = embeddings / embeddings.norm(dim=1, keepdim=True)  # normalize
+print(embeddings)

llm/inference/bce/run_bce-reranker.py

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+from mindspore import ops
+from mindnlp.transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification
+
+hf_token = 'your_hf_token'
+
+# init model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained('maidalun1020/bce-reranker-base_v1', token=hf_token)
+model = AutoModelForSequenceClassification.from_pretrained('maidalun1020/bce-reranker-base_v1', token=hf_token)
+
+# your query and corresponding passages
+query = "上海天气"
+passages = ["北京美食", "上海气候"]
+
+# construct sentence pairs
+sentence_pairs = [[query, passage] for passage in passages]
+
+# get inputs
+inputs = tokenizer(sentence_pairs, padding=True, truncation=True, max_length=512, return_tensors="ms")
+inputs_on_device = {k: v for k, v in inputs.items()}
+
+# calculate scores
+scores = model(**inputs_on_device, return_dict=True).logits.view(-1,).float()
+scores = ops.sigmoid(scores)
+print(scores)