Added files from espa-ai

README.md

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+---
+title: "ESPA AI"
+emoji: "🤖"
+colorFrom: "blue"
+colorTo: "green"
+sdk: "transformers"
+sdk_version: "4.21.1"
+app_file: "app.py"
+license: "mit"
+tags:
+  - "text-classification"
+  - "distilbert"
+  - "NLP"
+  - "sentiment-analysis"
+short_description: "A DistilBERT-based model fine-tuned on IMDb for text classification."
+---
+# Model Card for ESPA AI
+
+ESPA AI is a text classification model fine-tuned on the IMDb dataset using DistilBERT. It is designed to classify movie reviews as either positive or negative.
+
+## Model Details
+
+### Model Description
+
+This model uses the DistilBERT architecture, a smaller, faster version of BERT, to perform sentiment analysis on text data. It has been fine-tuned on the IMDb dataset for binary classification (positive or negative reviews).
+
+- **Developed by:** DilipKY
+- **Funded by:** [Optional Information]
+- **Model type:** Transformer-based model (DistilBERT)
+- **Language(s):** English
+- **License:** MIT License
+- **Finetuned from model:** distilbert-base-uncased
+
+### Model Sources
+
+- **Repository:** [DilipKY/espa-ai](https://huggingface.co/DilipKY/espa-ai)
+- **Paper:** [DistilBERT: A smaller, faster, cheaper version of BERT](https://arxiv.org/abs/1910.01108)
+
+## Uses
+
+### Direct Use
+
+This model can be used to classify text data into positive or negative categories. It is useful for sentiment analysis in applications like customer feedback analysis, review classification, etc.
+
+```python
+from transformers import pipeline
+
+# Load pre-trained model from Hugging Face
+classifier = pipeline("text-classification", model="DilipKY/espa-ai")
+
+# Test on a sample review
+sample_text = "This movie was amazing! The plot was so engaging and the acting was superb."
+result = classifier(sample_text)
+print(result)

config.json

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+{
+  "_name_or_path": "distilbert-base-uncased",
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "vocab_size": 30522
+}

model.safetensors

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