Auto-generated model card.

README.md

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+
+---
+language: en
+tags:
+- bert-finetuned-mrpc
+- sequence-classification
+license: unknown
+---
+
+# Bert-finetuned-mrpc Fine-tuned for Sequence classification
+
+This model is a fine-tuned version of [bert-finetuned-mrpc](https://huggingface.co/bert-finetuned-mrpc) for sequence classification tasks.
+
+## Model description
+
+- Model architecture: BertForSequenceClassification
+- Task: sequence-classification
+- Training dataset: bert-finetuned-mrpc
+- Number of parameters: 109,483,778
+- Sequence length: 512
+- Vocab size: 30522
+- Hidden size: 768
+- Number of attention heads: 12
+- Number of hidden layers: 12
+
+## Intended uses & limitations
+
+This model is intended for sequence classification tasks. It has been fine-tuned on a specific dataset, so its performance may vary on different datasets or domains.
+
+## Training procedure
+
+The model was fine-tuned using the following hyperparameters:
+{
+  "return_dict": true,
+  "output_hidden_states": false,
+  "output_attentions": false,
+  "torchscript": false,
+  "torch_dtype": "float32",
+  "use_bfloat16": false,
+  "tf_legacy_loss": false,
+  "pruned_heads": {},
+  "tie_word_embeddings": true,
+  "chunk_size_feed_forward": 0,
+  "is_encoder_decoder": false,
+  "is_decoder": false,
+  "cross_attention_hidden_size": null,
+  "add_cross_attention": false,
+  "tie_encoder_decoder": false,
+  "max_length": 20,
+  "min_length": 0,
+  "do_sample": false,
+  "early_stopping": false,
+  "num_beams": 1,
+  "num_beam_groups": 1,
+  "diversity_penalty": 0.0,
+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0,
+  "typical_p": 1.0,
+  "repetition_penalty": 1.0,
+  "length_penalty": 1.0,
+  "no_repeat_ngram_size": 0,
+  "encoder_no_repeat_ngram_size": 0,
+  "bad_words_ids": null,
+  "num_return_sequences": 1,
+  "output_scores": false,
+  "return_dict_in_generate": false,
+  "forced_bos_token_id": null,
+  "forced_eos_token_id": null,
+  "remove_invalid_values": false,
+  "exponential_decay_length_penalty": null,
+  "suppress_tokens": null,
+  "begin_suppress_tokens": null,
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "finetuning_task": null,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
+  },
+  "tokenizer_class": null,
+  "prefix": null,
+  "bos_token_id": null,
+  "pad_token_id": 0,
+  "eos_token_id": null,
+  "sep_token_id": null,
+  "decoder_start_token_id": null,
+  "task_specific_params": null,
+  "problem_type": "single_label_classification",
+  "_name_or_path": "bert-finetuned-mrpc",
+  "transformers_version": "4.38.1",
+  "gradient_checkpointing": false,
+  "model_type": "bert",
+  "vocab_size": 30522,
+  "hidden_size": 768,
+  "num_hidden_layers": 12,
+  "num_attention_heads": 12,
+  "hidden_act": "gelu",
+  "intermediate_size": 3072,
+  "hidden_dropout_prob": 0.1,
+  "attention_probs_dropout_prob": 0.1,
+  "max_position_embeddings": 512,
+  "type_vocab_size": 2,
+  "initializer_range": 0.02,
+  "layer_norm_eps": 1e-12,
+  "position_embedding_type": "absolute",
+  "use_cache": true,
+  "classifier_dropout": null
+}
+
+## Evaluation results
+
+[Evaluation results to be added]
+

model_card.py

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+from huggingface_hub import Repository, HfApi
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+import json
+
+# Initialize the Hugging Face API client
+api = HfApi()
+
+# Use the existing local repository
+repo = Repository("local-folder")
+repo.git_pull()
+
+# Load the model, tokenizer, and config from the existing checkpoint
+checkpoint_directory = "bert-finetuned-mrpc"
+model = AutoModelForSequenceClassification.from_pretrained(checkpoint_directory)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint_directory)
+config = AutoConfig.from_pretrained(checkpoint_directory)
+
+# Get the repository name
+repo_name = "amannagrawall002/bert-finetued-mrpc"  # Replace this with your actual repository name
+
+# Generate model card content
+model_name = config.name_or_path.split('/')[-1]
+task = list(config.task_specific_params.keys())[0] if hasattr(config, 'task_specific_params') and config.task_specific_params else "sequence-classification"
+architecture = config.architectures[0] if hasattr(config, 'architectures') and config.architectures else "Unknown"
+
+model_card_content = f"""
+---
+language: en
+tags:
+- {model_name}
+- {task}
+license: {config.license if hasattr(config, 'license') else 'unknown'}
+---
+
+# {model_name.capitalize()} Fine-tuned for {task.replace('-', ' ').capitalize()}
+
+This model is a fine-tuned version of [{model_name}](https://huggingface.co/{config.name_or_path}) for {task.replace('-', ' ')} tasks.
+
+## Model description
+
+- Model architecture: {architecture}
+- Task: {task}
+- Training dataset: {config._name_or_path if hasattr(config, '_name_or_path') else 'Unknown'}
+- Number of parameters: {model.num_parameters():,}
+- Sequence length: {config.max_position_embeddings}
+- Vocab size: {config.vocab_size}
+- Hidden size: {config.hidden_size}
+- Number of attention heads: {config.num_attention_heads}
+- Number of hidden layers: {config.num_hidden_layers}
+
+## Intended uses & limitations
+
+This model is intended for {task.replace('-', ' ')} tasks. It has been fine-tuned on a specific dataset, so its performance may vary on different datasets or domains.
+
+## Training procedure
+
+The model was fine-tuned using the following hyperparameters:
+{json.dumps(config.to_dict(), indent=2)}
+
+## Evaluation results
+
+[Evaluation results to be added]
+
+"""
+
+with open(f"{repo.local_dir}/README.md", "w") as f:
+    f.write(model_card_content)
+
+# Add inference API code
+# pipeline_code = f"""
+# from transformers import pipeline
+
+# def inference(text):
+#     classifier = pipeline("{task}", model="{repo_name}")
+#     result = classifier(text)
+#     return result
+# """
+
+# with open(f"{repo.local_dir}/api.py", "w") as f:
+#     f.write(pipeline_code)
+
+# Commit and push changes
+repo.git_add()
+repo.git_commit("Added auto-generated model card")
+repo.git_push()
+
+# Enable the Inference API
+# api.add_space_secret(repo_name, "INFERENCE_ENDPOINT", f"https://api-inference.huggingface.co/models/{repo_name}")
+
+print("Auto-generated model card.")