commit files to HF hub

config.json

@@ -0,0 +1,46 @@
+{
+  "_name_or_path": "sgugger/finetuned-bert-mrpc",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "custom_pipelines": {
+    "pair-classification": {
+      "impl": "__main__.PairClassificationPipeline",
+      "pt": [
+        "AutoModelForSequenceClassification"
+      ],
+      "tf": [
+        "TFAutoModelForSequenceClassification"
+      ]
+    }
+  },
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "not_equivalent",
+    "1": "equivalent"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "equivalent": 1,
+    "not_equivalent": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.23.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 28996
+}

pipeline.py

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+import numpy as np
+
+from transformers import Pipeline
+
+
+def softmax(outputs):
+    maxes = np.max(outputs, axis=-1, keepdims=True)
+    shifted_exp = np.exp(outputs - maxes)
+    return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
+
+
+class PairClassificationPipeline(Pipeline):
+
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_kwargs = {}
+        if "second_text" in kwargs:
+            preprocess_kwargs["second_text"] = kwargs["second_text"]
+        return preprocess_kwargs, {}, {}
+
+    def preprocess(self, text, second_text=None):
+        return self.tokenizer(text,
+                              text_pair=second_text,
+                              return_tensors=self.framework)
+
+    def _forward(self, model_inputs):
+        return self.model(**model_inputs)
+
+    def postprocess(self, model_outputs):
+        logits = model_outputs.logits[0].numpy()
+        probabilities = softmax(logits)
+
+        best_class = np.argmax(probabilities)
+        label = self.model.config.id2label[best_class]
+        score = probabilities[best_class].item()
+        logits = logits.tolist()
+        return {"label": label, "score": score, "logits": logits}
+
+
+from transformers.pipelines import PIPELINE_REGISTRY
+
+from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
+
+if __name__ == "__main__":
+    PIPELINE_REGISTRY.register_pipeline(
+        "pair-classification",
+        pipeline_class=PairClassificationPipeline,
+        pt_model=AutoModelForSequenceClassification,
+        tf_model=TFAutoModelForSequenceClassification,
+    )
+
+    from transformers import pipeline
+
+    classifier = pipeline("pair-classification",
+                          model="sgugger/finetuned-bert-mrpc")
+    from huggingface_hub import Repository
+
+    repo = Repository("test-dynamic-pipeline",
+                      clone_from="paulhindemith/test-dynamic-pipeline")
+    classifier.save_pretrained("test-dynamic-pipeline")
+    repo.push_to_hub()

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3301f183527431290cc7ca96700df70254ba4828c1936189df6dbe30cb88f7e9
+size 433317237

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,14 @@
+{
+  "cls_token": "[CLS]",
+  "do_lower_case": false,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "name_or_path": "sgugger/finetuned-bert-mrpc",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": null,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}