PaddlePaddle Hackathon 54 提交

community/junnyu/ckiplab-bert-base-chinese-ner/README.md

@@ -4,6 +4,7 @@
 关于完整使用方法及其他信息，请参考 https://github.com/ckiplab/ckip-transformers 。
 
 **模型结构**： **`BertForTokenClassification`**，带有token分类头的Bert模型。
+
 **适用下游任务**：**命名实体识别**，该权重已经在下游`NER`任务上进行了微调，因此可直接使用。
 
 # 使用示例

community/junnyu/ckiplab-bert-base-chinese-pos/README.md

@@ -4,6 +4,7 @@
 关于完整使用方法及其他信息，请参考 https://github.com/ckiplab/ckip-transformers 。
 
 **模型结构**： **`BertForTokenClassification`**，带有token分类头的Bert模型。
+
 **适用下游任务**：**词性标注**，该权重已经在下游`POS`任务上进行了微调，因此可直接使用。
 
 # 使用示例

community/junnyu/ckiplab-bert-base-chinese-ws/README.md

@@ -4,6 +4,7 @@
 关于完整使用方法及其他信息，请参考 https://github.com/ckiplab/ckip-transformers 。
 
 **模型结构**： **`BertForTokenClassification`**，带有token分类头的Bert模型。
+
 **适用下游任务**：**分词**，该权重已经在下游`WS`任务上进行了微调，因此可直接使用。
 
 # 使用示例

community/junnyu/electra_compare.py

@@ -0,0 +1,138 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import paddle
+import torch
+import numpy as np
+import paddlenlp.transformers as ppnlp
+import transformers as hgnlp
+
+
+def compare(a, b):
+    a = a.cpu().numpy()
+    b = b.cpu().numpy()
+    meandif = np.abs(a - b).mean()
+    maxdif = np.abs(a - b).max()
+    print("mean dif:", meandif)
+    print("max dif:", maxdif)
+
+
+def compare_discriminator(
+        path="junnyu/hfl-chinese-electra-180g-base-discriminator"):
+    pdmodel = ppnlp.ElectraDiscriminator.from_pretrained(path)
+    ptmodel = ppnlp.ElectraForPreTraining.from_pretrained(path).cuda()
+    tokenizer = ppnlp.ElectraTokenizer.from_pretrained(path)
+    pdmodel.eval()
+    ptmodel.eval()
+    text = "欢迎使用paddlenlp！"
+    pdinputs = {
+        k: paddle.to_tensor(
+            v, dtype="int64").unsqueeze(0)
+        for k, v in tokenizer(text).items()
+    }
+    ptinputs = {
+        k: torch.tensor(
+            v, dtype=torch.long).unsqueeze(0).cuda()
+        for k, v in tokenizer(text).items()
+    }
+    with paddle.no_grad():
+        pd_logits = pdmodel(**pdinputs)
+
+    with torch.no_grad():
+        pt_logits = ptmodel(**ptinputs).logits
+
+    compare(pd_logits, pt_logits)
+
+
+def compare_generator():
+    text = "本院经审查认为，本案[MASK]民间借贷纠纷申请再审案件，应重点审查二审判决是否存在错误的情形。"
+    # ppnlp
+    path = "junnyu/hfl-chinese-legal-electra-small-generator"
+    model = ppnlp.ElectraForMaskedLM.from_pretrained(path)
+    tokenizer = ppnlp.ElectraTokenizer.from_pretrained(path)
+    model.eval()
+    tokens = ["[CLS]"]
+    text_list = text.split("[MASK]")
+    for i, t in enumerate(text_list):
+        tokens.extend(tokenizer.tokenize(t))
+        if i == len(text_list) - 1:
+            tokens.extend(["[SEP]"])
+        else:
+            tokens.extend(["[MASK]"])
+
+    input_ids_list = tokenizer.convert_tokens_to_ids(tokens)
+    input_ids = paddle.to_tensor([input_ids_list])
+    with paddle.no_grad():
+        pd_outputs = model(input_ids)[0]
+    pd_outputs_sentence = "paddle: "
+    for i, id in enumerate(input_ids_list):
+        if id == tokenizer.convert_tokens_to_ids(["[MASK]"])[0]:
+            scores, index = paddle.nn.functional.softmax(pd_outputs[i],
+                                                         -1).topk(5)
+            tokens = tokenizer.convert_ids_to_tokens(index.tolist())
+            outputs = []
+            for score, tk in zip(scores.tolist(), tokens):
+                outputs.append(f"{tk}={score}")
+            pd_outputs_sentence += "[" + "||".join(outputs) + "]" + " "
+        else:
+            pd_outputs_sentence += "".join(
+                tokenizer.convert_ids_to_tokens(
+                    [id], skip_special_tokens=True)) + " "
+
+    print(pd_outputs_sentence)
+
+    # transformers
+    path = "hfl/chinese-legal-electra-small-generator"
+    config = hgnlp.ElectraConfig.from_pretrained(path)
+    config.hidden_size = 64
+    config.intermediate_size = 256
+    config.num_attention_heads = 1
+    model = hgnlp.ElectraForMaskedLM.from_pretrained(path, config=config)
+    tokenizer = hgnlp.ElectraTokenizer.from_pretrained(path)
+    model.eval()
+
+    inputs = tokenizer(text, return_tensors="pt")
+
+    with torch.no_grad():
+        pt_outputs = model(**inputs).logits[0]
+    pt_outputs_sentence = "pytorch: "
+    for i, id in enumerate(inputs["input_ids"][0].tolist()):
+        if id == tokenizer.convert_tokens_to_ids(["[MASK]"])[0]:
+            scores, index = torch.nn.functional.softmax(pt_outputs[i],
+                                                        -1).topk(5)
+            tokens = tokenizer.convert_ids_to_tokens(index.tolist())
+            outputs = []
+            for score, tk in zip(scores.tolist(), tokens):
+                outputs.append(f"{tk}={score}")
+            pt_outputs_sentence += "[" + "||".join(outputs) + "]" + " "
+        else:
+            pt_outputs_sentence += "".join(
+                tokenizer.convert_ids_to_tokens(
+                    [id], skip_special_tokens=True)) + " "
+
+    print(pt_outputs_sentence)
+
+
+if __name__ == "__main__":
+    compare_discriminator(
+        path="junnyu/hfl-chinese-electra-180g-base-discriminator")
+    # # mean dif: 3.1698835e-06
+    # # max dif: 1.335144e-05
+    compare_discriminator(
+        path="junnyu/hfl-chinese-electra-180g-small-ex-discriminator")
+    # mean dif: 3.7930229e-06
+    # max dif: 1.04904175e-05
+    compare_generator()
+    # paddle:  本 院 经 审 查 认 为 ， 本 案 [因=0.27444931864738464||经=0.18613006174564362||系=0.09408623725175858||的=0.07536833733320236||就=0.033634234219789505] 民 间 借 贷 纠 纷 申 请 再 审 案 件 ， 应 重 点 审 查 二 审 判 决 是 否 存 在 错 误 的 情 形 。
+    # pytorch:  本 院 经 审 查 认 为 ， 本 案 [因=0.2744344472885132||经=0.1861187219619751||系=0.09407979995012283||的=0.07537488639354706||就=0.03363779932260513] 民 间 借 贷 纠 纷 申 请 再 审 案 件 ， 应 重 点 审 查 二 审 判 决 是 否 存 在 错 误 的 情 形 。  

community/junnyu/electra_convert_huggingface2paddle.py

@@ -0,0 +1,79 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections import OrderedDict
+import argparse
+
+huggingface_to_paddle = {
+    "embeddings.LayerNorm": "embeddings.layer_norm",
+    "encoder.layer": "encoder.layers",
+    "attention.self.query.": "self_attn.q_proj.",
+    "attention.self.key.": "self_attn.k_proj.",
+    "attention.self.value.": "self_attn.v_proj.",
+    "attention.output.dense.": "self_attn.out_proj.",
+    "intermediate.dense": "linear1",
+    "output.dense": "linear2",
+    "attention.output.LayerNorm": "norm1",
+    "output.LayerNorm": "norm2",
+    "generator_predictions.LayerNorm": "generator_predictions.layer_norm",
+    "generator_lm_head.bias": "generator_lm_head_bias",
+}
+
+skip_weights = ["electra.embeddings.position_ids"]
+dont_transpose = ["_embeddings.weight", "LayerNorm."]
+
+
+def convert_pytorch_checkpoint_to_paddle(pytorch_checkpoint_path,
+                                         paddle_dump_path):
+    import torch
+    import paddle
+    pytorch_state_dict = torch.load(pytorch_checkpoint_path, map_location="cpu")
+    paddle_state_dict = OrderedDict()
+    for k, v in pytorch_state_dict.items():
+        if k == "generator_lm_head.weight": continue
+        is_transpose = False
+        if k in skip_weights:
+            continue
+        if k[-7:] == ".weight":
+            if not any([w in k for w in dont_transpose]):
+                if v.ndim == 2:
+                    v = v.transpose(0, 1)
+                    is_transpose = True
+        oldk = k
+        for huggingface_name, paddle_name in huggingface_to_paddle.items():
+            k = k.replace(huggingface_name, paddle_name)
+
+        print(f"Converting: {oldk} => {k} | is_transpose {is_transpose}")
+        paddle_state_dict[k] = v.data.numpy()
+
+    paddle.save(paddle_state_dict, paddle_dump_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--pytorch_checkpoint_path",
+        default=r"MODEL\hfl-chinese-electra-180g-base-discriminator\pytorch_model.bin",
+        type=str,
+        required=False,
+        help="Path to the Pytorch checkpoint path.")
+    parser.add_argument(
+        "--paddle_dump_path",
+        default=r"MODEL\hfl-chinese-electra-180g-base-discriminator\model_state.pdparams",
+        type=str,
+        required=False,
+        help="Path to the output Paddle model.")
+    args = parser.parse_args()
+    convert_pytorch_checkpoint_to_paddle(args.pytorch_checkpoint_path,
+                                         args.paddle_dump_path)

community/junnyu/hfl-chinese-electra-180g-base-discriminator/README.md

@@ -0,0 +1,37 @@
+# 详细介绍
+**介绍**：该模型是base版本的Electra discriminator模型，并且在180G的中文数据上进行训练。
+
+**模型结构**： **`ElectraDiscriminator`**，带有判别器的中文Electra模型。
+
+**适用下游任务**：**通用下游任务**，如：句子级别分类，token级别分类，抽取式问答等任务。
+
+# 使用示例
+
+```python
+import paddle
+from paddlenlp.transformers import ElectraDiscriminator, ElectraTokenizer
+
+path = "junnyu/hfl-chinese-electra-180g-base-discriminator"
+model = ElectraDiscriminator.from_pretrained(path)
+tokenizer = ElectraTokenizer.from_pretrained(path)
+model.eval()
+
+text = "欢迎使用paddlenlp！"
+inputs = {
+    k: paddle.to_tensor(
+        v, dtype="int64").unsqueeze(0)
+    for k, v in tokenizer(text).items()
+}
+
+with paddle.no_grad():
+    logits = model(**inputs)
+
+print(logits.shape)
+
+```
+
+# 权重来源
+
+https://huggingface.co/hfl/chinese-electra-180g-base-discriminator
+谷歌和斯坦福大学发布了一种名为 ELECTRA 的新预训练模型，与 BERT 及其变体相比，该模型具有非常紧凑的模型尺寸和相对具有竞争力的性能。 为进一步加快中文预训练模型的研究，HIT与科大讯飞联合实验室（HFL）发布了基于ELECTRA官方代码的中文ELECTRA模型。 与 BERT 及其变体相比，ELECTRA-small 只需 1/10 的参数就可以在几个 NLP 任务上达到相似甚至更高的分数。
+这个项目依赖于官方ELECTRA代码: https://github.com/google-research/electra

community/junnyu/hfl-chinese-electra-180g-base-discriminator/files.json

@@ -0,0 +1,6 @@
+{
+  "model_config_file": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-electra-180g-base-discriminator/model_config.json",
+  "model_state": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-electra-180g-base-discriminator/model_state.pdparams",
+  "tokenizer_config_file": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-electra-180g-base-discriminator/tokenizer_config.json",
+  "vocab_file": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-electra-180g-base-discriminator/vocab.txt"
+}

community/junnyu/hfl-chinese-electra-180g-small-ex-discriminator/README.md

@@ -0,0 +1,36 @@
+# 详细介绍
+**介绍**：该模型是small版本的Electra discriminator模型，并且在180G的中文数据上进行训练。
+
+**模型结构**： **`ElectraDiscriminator`**，带有判别器的中文Electra模型。
+
+**适用下游任务**：**通用下游任务**，如：句子级别分类，token级别分类，抽取式问答等任务。
+
+# 使用示例
+
+```python
+import paddle
+from paddlenlp.transformers import ElectraDiscriminator,ElectraTokenizer
+
+path = "junnyu/hfl-chinese-electra-180g-small-ex-discriminator"
+model = ElectraDiscriminator.from_pretrained(path)
+tokenizer = ElectraTokenizer.from_pretrained(path)
+model.eval()
+
+text = "欢迎使用paddlenlp！"
+inputs = {
+    k: paddle.to_tensor(
+        v, dtype="int64").unsqueeze(0)
+    for k, v in tokenizer(text).items()
+}
+
+with paddle.no_grad():
+    logits = model(**inputs)
+
+print(logits.shape)
+
+```
+
+# 权重来源
+
+https://huggingface.co/hfl/chinese-electra-180g-small-ex-discriminator
+谷歌和斯坦福大学发布了一种名为 ELECTRA 的新预训练模型，与 BERT 及其变体相比，该模型具有非常紧凑的模型尺寸和相对具有竞争力的性能。 为进一步加快中文预训练模型的研究，HIT与科大讯飞联合实验室（HFL）发布了基于ELECTRA官方代码的中文ELECTRA模型。 与 BERT 及其变体相比，ELECTRA-small 只需 1/10 的参数就可以在几个 NLP 任务上达到相似甚至更高的分数。

community/junnyu/hfl-chinese-electra-180g-small-ex-discriminator/files.json

@@ -0,0 +1,6 @@
+{
+  "model_config_file": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-electra-180g-small-ex-discriminator/model_config.json",
+  "model_state": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-electra-180g-small-ex-discriminator/model_state.pdparams",
+  "tokenizer_config_file": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-electra-180g-small-ex-discriminator/tokenizer_config.json",
+  "vocab_file": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-electra-180g-small-ex-discriminator/vocab.txt"
+}

community/junnyu/hfl-chinese-legal-electra-small-generator/README.md

@@ -0,0 +1,58 @@
+# 详细介绍
+**介绍**：该模型是small版本的Electra generator模型，该模型在法律领域数据上进行了预训练。
+
+**模型结构**： **`ElectraGenerator`**，带有生成器的中文Electra模型。
+
+**适用下游任务**：**法律领域的下游任务**，如：法律领域的句子级别分类，法律领域的token级别分类，法律领域的抽取式问答等任务。
+（注：生成器的效果不好，通常使用判别器进行下游任务微调）
+
+
+# 使用示例
+
+```python
+import paddle
+from paddlenlp.transformers import ElectraGenerator, ElectraTokenizer
+
+text = "本院经审查认为，本案[MASK]民间借贷纠纷申请再审案件，应重点审查二审判决是否存在错误的情形。"
+path = "junnyu/hfl-chinese-legal-electra-small-generator"
+model = ElectraGenerator.from_pretrained(path)
+model.eval()
+tokenizer = ElectraTokenizer.from_pretrained(path)
+
+tokens = ["[CLS]"]
+text_list = text.split("[MASK]")
+for i, t in enumerate(text_list):
+    tokens.extend(tokenizer.tokenize(t))
+    if i == len(text_list) - 1:
+        tokens.extend(["[SEP]"])
+    else:
+        tokens.extend(["[MASK]"])
+
+input_ids_list = tokenizer.convert_tokens_to_ids(tokens)
+input_ids = paddle.to_tensor([input_ids_list])
+with paddle.no_grad():
+    pd_outputs = model(input_ids)[0]
+pd_outputs_sentence = "paddle: "
+for i, id in enumerate(input_ids_list):
+    if id == tokenizer.convert_tokens_to_ids(["[MASK]"])[0]:
+        scores, index = paddle.nn.functional.softmax(pd_outputs[i],
+                                                        -1).topk(5)
+        tokens = tokenizer.convert_ids_to_tokens(index.tolist())
+        outputs = []
+        for score, tk in zip(scores.tolist(), tokens):
+            outputs.append(f"{tk}={score}")
+        pd_outputs_sentence += "[" + "||".join(outputs) + "]" + " "
+    else:
+        pd_outputs_sentence += "".join(
+            tokenizer.convert_ids_to_tokens(
+                [id], skip_special_tokens=True)) + " "
+
+print(pd_outputs_sentence)
+# paddle:  本 院 经 审 查 认 为 ， 本 案 [因=0.27444931864738464||经=0.18613006174564362||系=0.09408623725175858||的=0.07536833733320236||就=0.033634234219789505] 民 间 借 贷 纠 纷 申 请 再 审 案 件 ， 应 重 点 审 查 二 审 判 决 是 否 存 在 错 误 的 情 形 。
+```
+
+# 权重来源
+
+https://huggingface.co/hfl/chinese-legal-electra-small-generator
+谷歌和斯坦福大学发布了一种名为 ELECTRA 的新预训练模型，与 BERT 及其变体相比，该模型具有非常紧凑的模型尺寸和相对具有竞争力的性能。 为进一步加快中文预训练模型的研究，HIT与科大讯飞联合实验室（HFL）发布了基于ELECTRA官方代码的中文ELECTRA模型。 与 BERT 及其变体相比，ELECTRA-small 只需 1/10 的参数就可以在几个 NLP 任务上达到相似甚至更高的分数。
+这个项目依赖于官方ELECTRA代码: https://github.com/google-research/electra

community/junnyu/hfl-chinese-legal-electra-small-generator/files.json

@@ -0,0 +1,6 @@
+{
+  "model_config_file": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-legal-electra-small-generator/model_config.json",
+  "model_state": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-legal-electra-small-generator/model_state.pdparams",
+  "tokenizer_config_file": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-legal-electra-small-generator/tokenizer_config.json",
+  "vocab_file": "https://paddlenlp.bj.bcebos.com/models/transformers/community/junnyu/hfl-chinese-legal-electra-small-generator/vocab.txt"
+}