Upload README.md

README.md

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-license: apache-2.0
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+# DeBERTa-v3 Twitter Sentiment Models
+
+This page contains one of two DeBERTa-v3 models (xsmall and base) fine-tuned for Twitter sentiment regression.
+
+## Model Details
+
+- **Model Architecture**: DeBERTa-v3
+- **Variants**: 
+  - xsmall (22M parameters)
+  - base (86M parameters)
+- **Task**: Sentiment regression
+- **Language**: English
+- **License**: [Model license]
+
+## Intended Use
+
+These models are designed for fine-grained sentiment analysis of English tweets. They output a **continuous sentiment score** rather than discrete categories.
+- negative score means negative sentiment
+- 0 score means neutral sentiment
+- positive score means positive sentiment
+- the absolute value of the score represents how strong that sentiment is
+
+## Training Data
+
+The models were fine-tuned on a dataset of English tweets collected between September 2009 and January 2010. The sentiment scores were derived from a meta-analysis of 10 different sentiment classifiers using principal component analysis. Find the dataset at [agentlans/twitter-sentiment-meta-analysis](https://huggingface.co/datasets/agentlans/twitter-sentiment-meta-analysis).
+
+## How to use
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name="agentlans/deberta-v3-xsmall-tweet-sentiment"
+
+# Put model on GPU or else CPU
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+
+def sentiment(text):
+    """Processes the text using the model and returns its logits.
+    In this case, it's interpreted as the sentiment score for that text."""
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True).to(device)
+    with torch.no_grad():
+        logits = model(**inputs).logits.squeeze().cpu()
+    return logits.tolist()
+
+# Example usage
+text = [x.strip() for x in """
+    I absolutely despise this product and regret ever purchasing it.
+    The service at that restaurant was terrible and ruined our entire evening.
+    I'm feeling a bit under the weather today, but it's not too bad.
+    The weather is quite average today, neither good nor bad.
+    The movie was okay, I didn't love it but I didn't hate it either.
+    I'm looking forward to the weekend, it should be nice to relax.
+    This new coffee shop has a really pleasant atmosphere and friendly staff.
+    I'm thrilled with my new job and the opportunities it presents!
+    The concert last night was absolutely incredible, easily the best I've ever seen.
+    I'm overjoyed and grateful for all the love and support from my friends and family.
+""".strip().split("\n")]
+
+for x, s in zip(text, sentiment(text)):
+    print(f"Text: {x}\nSentiment: {s}\n")
+```
+
+## Performance
+
+Evaluation set RMSE:
+- xsmall: 0.2560
+- base: 0.1938
+
+## Limitations
+
+- English language only
+- Trained specifically on tweets, may or may not generalize well to other text types
+- Lack of broader context beyond individual tweets
+- May struggle with detecting sarcasm or nuanced sentiment
+
+## Ethical Considerations
+
+- Potential biases in the training data related to the time period and Twitter user demographics
+- Risk of misuse for large-scale sentiment monitoring without consent