Init Model

README.md

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+---
+license: cc-by-nc-4.0
+language:
+- en
+tags:
+- English
+- RoBERTa-base
+- Text Classification
+pipeline_tag: text-classification
+---
+
+# RoBERTa base Fine-Tuned for Proposal Sentence Classification
+
+## Overview
+
+- **Language**: English
+- **Model Name**: oeg/RoBERTa_Repository_Proposal
+  
+## Description
+
+This model is a fine-tuned RoBERTa-base model trained to classify sentences into two classes: proposal and non-proposal sentences. The training data includes sentences proposing a software or data repository. The model is trained to recognize and classify these sentences accurately.
+
+## How to use
+
+To use this model in Python:
+
+```python
+from transformers import RobertaForSequenceClassification, RobertaTokenizer
+import torch
+
+tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
+model = RobertaForSequenceClassification.from_pretrained("oeg/RoBERTa_Repository_Proposal/roberta-model")
+
+sentence = "Your input sentence here."
+inputs = tokenizer(sentence, return_tensors="pt")
+outputs = model(**inputs)
+probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)

roberta-model/config.json

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+{
+  "_name_or_path": "roberta-base",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

roberta-model/model.safetensors

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