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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- agentlans/text-quality-v2
+language:
+- en
+base_model:
+- microsoft/deberta-v3-xsmall
+pipeline_tag: text-classification
+---
+# DeBERTa v3 for Text Quality Assessment
+
+## Model Details
+
+- **Model Architecture:** DeBERTa v3 (xsmall and base variants)
+- **Task:** Text quality assessment (regression)
+- **Training Data:** Text Quality Meta-Analysis Dataset at [agentlans/text-quality-v2](https://huggingface.co/datasets/agentlans/text-quality-v2)
+- **Output:** Single continuous value representing text quality
+
+## Intended Use
+
+These models are designed to assess the quality of English text, where "quality" refers to legible sentences that are not spam and contain useful information. They can be used for:
+
+- Content moderation
+- Spam detection
+- Information quality assessment
+- Text filtering
+
+## Usage
+
+The models accept text input and return a single continuous value representing the assessed quality. Higher values indicate higher perceived quality. Example usage is provided in the code snippet.
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+model_name="agentlans/deberta-v3-xsmall-quality-v2"
+
+# 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 quality(text):
+    """Processes the text using the model and returns its logits.
+    In this case, it's interpreted as the the combined quality 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 """
+Congratulations! You've won a $1,000 gift card! Click here to claim your prize now!!!
+Page 1 2 3 4 5 Next Last>>
+Urgent: Your account has been compromised! Click this link to verify your identity and secure your account immediately!!!
+Today marks a significant milestone in our journey towards sustainability! 🌍✨ We’re excited to announce our partnership with local organizations to plant 10,000 trees in our community this fall. Join us in making a positive impact on our environment!
+In recent years, the impact of climate change has become increasingly evident, affecting ecosystems and human livelihoods across the globe.
+The mitochondria is the powerhouse of the cell.
+Exclusive discount on Super MitoMax Energy Boost! Recharge your mitochondria today!
+Everyone is talking about this new diet that guarantees weight loss without exercise!
+Discover five tips for improving your productivity while working from home.
+""".strip().split("\n")]
+
+result = quality(text)
+for x, s in zip(text, result):
+    print(f"Text: {x}\nQuality: {round(s, 2)}\n")
+```
+
+Example output for the `xsmall` size model:
+```
+Text: Congratulations! You've won a $1,000 gift card! Click here to claim your prize now!!!
+Quality: -0.77
+
+Text: Page 1 2 3 4 5 Next Last>>
+Quality: -1.36
+
+Text: Urgent: Your account has been compromised! Click this link to verify your identity and secure your account immediately!!!
+Quality: -1.17
+
+Text: Today marks a significant milestone in our journey towards sustainability! 🌍✨ We’re excited to announce our partnership with local organizations to plant 10,000 trees in our community this fall. Join us in making a positive impact on our environment!
+Quality: -0.59
+
+Text: In recent years, the impact of climate change has become increasingly evident, affecting ecosystems and human livelihoods across the globe.
+Quality: 0.58
+
+Text: The mitochondria is the powerhouse of the cell.
+Quality: 0.39
+
+Text: Exclusive discount on Super MitoMax Energy Boost! Recharge your mitochondria today!
+Quality: -1.01
+
+Text: Everyone is talking about this new diet that guarantees weight loss without exercise!
+Quality: 0.57
+
+Text: Discover five tips for improving your productivity while working from home.
+Quality: 0.45
+```
+
+## Performance Metrics
+
+Root mean squared error (RMSE) on 20% held-out evaluation set:
+- **DeBERTa v3 xsmall:** 0.6296
+- **DeBERTa v3 base:** 0.5038
+
+The base model outperforms the xsmall variant in terms of accuracy.
+
+## Limitations and Biases
+
+- The models are trained on a specific dataset and may not generalize well to all types of text or domains.
+- "Quality" is a subjective concept, and the models' assessments may not align with all human judgments.
+- The models may exhibit biases present in the training data.
+  - For example, there is a bias against self-help, promotional, and public relations material.
+- They do not assess factual correctness or grammatical accuracy.
+
+## Ethical Considerations
+
+- These models should not be used as the sole determinant for content moderation or censorship.
+- Care should be taken to avoid reinforcing existing biases in content selection or promotion.
+- The models' outputs should be interpreted as suggestions rather than definitive judgments.
+
+## Caveats and Recommendations
+
+- Use these models in conjunction with other tools and human oversight for content moderation.
+- Regularly evaluate the models' performance on your specific use case and data.
+- Be aware that the models may not perform equally well across all text types or domains.
+- Consider fine-tuning the models on domain-specific data for improved performance in specialized applications.

added_tokens.json

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+{
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+}

config.json

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+{
+  "_name_or_path": "microsoft/deberta-v3-xsmall",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
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+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
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+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 6,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 384,
+  "pos_att_type": [
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+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.45.1",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

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special_tokens_map.json

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+    "lstrip": false,
+    "normalized": true,
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+}

spm.model

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tokenizer_config.json

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+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "sp_model_kwargs": {},
+  "split_by_punct": false,
+  "tokenizer_class": "DebertaV2Tokenizer",
+  "unk_token": "[UNK]",
+  "vocab_type": "spm"
+}