community models: fix testing bug (#5678)

* update project

* update icon and keyword

* fix running bug

* fix conflict

modelcenter/community/allenai/macaw-large/introduction_cn.ipynb

@@ -53,7 +53,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"allenai/macaw-large\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/allenai/macaw-large/introduction_en.ipynb

@@ -53,7 +53,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"allenai/macaw-large\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/allenai/specter/introduction_cn.ipynb

@@ -44,9 +44,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertModel\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"allenai/specter\")\n",
+    "model = BertModel.from_pretrained(\"allenai/specter\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/allenai/specter/introduction_en.ipynb

@@ -44,9 +44,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertModel\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"allenai/specter\")\n",
+    "model = BertModel.from_pretrained(\"allenai/specter\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/cross-encoder/ms-marco-MiniLM-L-12-v2/introduction_cn.ipynb

@@ -50,9 +50,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertForSequenceClassification\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"cross-encoder/ms-marco-MiniLM-L-12-v2\")\n",
+    "model = BertForSequenceClassification.from_pretrained(\"cross-encoder/ms-marco-MiniLM-L-12-v2\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/cross-encoder/ms-marco-MiniLM-L-12-v2/introduction_en.ipynb

@@ -50,9 +50,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertForSequenceClassification\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"cross-encoder/ms-marco-MiniLM-L-12-v2\")\n",
+    "model = BertForSequenceClassification.from_pretrained(\"cross-encoder/ms-marco-MiniLM-L-12-v2\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/cross-encoder/ms-marco-TinyBERT-L-2/introduction_cn.ipynb

@@ -50,9 +50,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertModel\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"cross-encoder/ms-marco-TinyBERT-L-2\")\n",
+    "model = BertModel.from_pretrained(\"cross-encoder/ms-marco-TinyBERT-L-2\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]
@@ -103,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

modelcenter/community/cross-encoder/ms-marco-TinyBERT-L-2/introduction_en.ipynb

@@ -50,9 +50,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertModel\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"cross-encoder/ms-marco-TinyBERT-L-2\")\n",
+    "model = BertModel.from_pretrained(\"cross-encoder/ms-marco-TinyBERT-L-2\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]
@@ -103,7 +103,11 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

modelcenter/community/cross-encoder/nli-MiniLM2-L6-H768/introduction_cn.ipynb

@@ -61,9 +61,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import RobertaForSequenceClassification\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"cross-encoder/nli-MiniLM2-L6-H768\")\n",
+    "model = RobertaForSequenceClassification.from_pretrained(\"cross-encoder/nli-MiniLM2-L6-H768\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/cross-encoder/nli-MiniLM2-L6-H768/introduction_en.ipynb

@@ -61,9 +61,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import RobertaForSequenceClassification\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"cross-encoder/nli-MiniLM2-L6-H768\")\n",
+    "model = RobertaForSequenceClassification.from_pretrained(\"cross-encoder/nli-MiniLM2-L6-H768\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/cross-encoder/stsb-TinyBERT-L-4/introduction_cn.ipynb

@@ -156,9 +156,9 @@
    ],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertForSequenceClassification\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"cross-encoder/stsb-TinyBERT-L-4\")\n",
+    "model = BertForSequenceClassification.from_pretrained(\"cross-encoder/stsb-TinyBERT-L-4\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/cross-encoder/stsb-TinyBERT-L-4/introduction_en.ipynb

@@ -52,9 +52,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertForSequenceClassification\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"cross-encoder/stsb-TinyBERT-L-4\")\n",
+    "model = BertForSequenceClassification.from_pretrained(\"cross-encoder/stsb-TinyBERT-L-4\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/emilyalsentzer/Bio_ClinicalBERT/introduction_cn.ipynb

@@ -50,9 +50,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertForMaskedLM\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"emilyalsentzer/Bio_ClinicalBERT\")\n",
+    "model = BertForMaskedLM.from_pretrained(\"emilyalsentzer/Bio_ClinicalBERT\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/emilyalsentzer/Bio_ClinicalBERT/introduction_en.ipynb

@@ -50,9 +50,9 @@
    "outputs": [],
    "source": [
     "import paddle\n",
-    "from paddlenlp.transformers import AutoModel\n",
+    "from paddlenlp.transformers import BertForMaskedLM\n",
     "\n",
-    "model = AutoModel.from_pretrained(\"emilyalsentzer/Bio_ClinicalBERT\")\n",
+    "model = BertForMaskedLM.from_pretrained(\"emilyalsentzer/Bio_ClinicalBERT\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
     "print(model(input_ids))"
    ]

modelcenter/community/google/t5-base-lm-adapt/introduction_cn.ipynb

@@ -66,7 +66,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-base-lm-adapt\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-base-lm-adapt/introduction_en.ipynb

@@ -66,7 +66,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-base-lm-adapt\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-large-lm-adapt/introduction_cn.ipynb

@@ -74,7 +74,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-large-lm-adapt\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-large-lm-adapt/introduction_en.ipynb

@@ -74,7 +74,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-large-lm-adapt\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-small-lm-adapt/introduction_cn.ipynb

@@ -74,7 +74,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-small-lm-adapt\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-small-lm-adapt/introduction_en.ipynb

@@ -74,7 +74,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-small-lm-adapt\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-v1_1-base/introduction_cn.ipynb

@@ -65,7 +65,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-v1_1-base\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-v1_1-base/introduction_en.ipynb

@@ -65,7 +65,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-v1_1-base\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-v1_1-large/introduction_cn.ipynb

@@ -65,7 +65,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-v1_1-large\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-v1_1-large/introduction_en.ipynb

@@ -65,7 +65,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-v1_1-large\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-v1_1-small/introduction_cn.ipynb

@@ -67,7 +67,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-v1_1-small\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {

modelcenter/community/google/t5-v1_1-small/introduction_en.ipynb

@@ -67,7 +67,8 @@
     "\n",
     "model = AutoModel.from_pretrained(\"google/t5-v1_1-small\")\n",
     "input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
-    "print(model(input_ids))"
+    "decoder_input_ids = paddle.randint(100, 200, shape=[1, 20])\n",
+    "print(model(input_ids, decoder_input_ids=decoder_input_ids))"
    ]
   },
   {