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Model: ChangeIsKey/llama3-janus
Source: Original Platform

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README.md

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+---
+language:
+- en
+base_model:
+- meta-llama/Meta-Llama-3-8B
+pipeline_tag: text2text-generation
+---
+
+## Janus
+(Built with Meta Llama 3)
+
+For the version with the PoS tag visit [Janus (PoS)](https://huggingface.co/ChangeIsKey/llama3-janus-pos).
+
+### Model Details
+- **Model Name**: Janus
+- **Version**: 1.0
+- **Developers**: Pierluigi Cassotti, Nina Tahmasebi
+- **Affiliation**: University of Gothenburg
+- **License**: MIT
+- **GitHub Repository**: [Historical Word Usage Generation](https://github.com/ChangeIsKey/historical-word-usage-generation)
+- **Paper**: [Sense-specific Historical Word Usage Generation](https://transacl.org)
+- **Contact**: pierluigi.cassotti@gu.se
+
+### Model Description
+Janus is a fine-tuned **Llama 3 8B** model designed to generate historically and semantically accurate word usages. It takes as input a word, its sense definition, and a year and produces example sentences that reflect linguistic usage from the specified period. This model is particularly useful for **semantic change detection**, **historical NLP**, and **linguistic research**.
+
+### Intended Use
+- **Semantic Change Detection**: Investigating how word meanings evolve over time.
+- **Historical Text Processing**: Enhancing the understanding and modeling of historical texts.
+- **Corpus Expansion**: Generating sense-annotated corpora for linguistic studies.
+
+### Training Data
+- **Dataset**: Extracted from the **Oxford English Dictionary (OED)**
+- **Size**: Over **1.2 million** sense-annotated historical usages
+- **Time Span**: **1700 - 2020**
+- **Data Format**:
+  ```
+  <year><|t|><lemma><|t|><definition><|s|><historical usage sentence><|end|>
+  ```
+- **Janus (PoS) Format**:
+  ```
+  <year><|t|><lemma><|t|><definition><|p|><PoS><|p|><|s|><historical usage sentence><|end|>
+  ```
+
+### Training Procedure
+- **Base Model**: `meta-llama/Llama-3-8B`
+- **Optimization**: **QLoRA** (Quantized Low-Rank Adaptation)
+- **Batch Size**: **4**
+- **Learning Rate**: **2e-4**
+- **Epochs**: **1**
+
+### Model Performance
+- **Temporal Accuracy**: Root mean squared error (RMSE) of **~52.7 years** (close to OED ground truth)
+- **Semantic Accuracy**: Comparable to OED test data on human evaluations
+- **Context Variability**: Low lexical repetition, preserving natural linguistic diversity
+
+### Usage Example
+#### Generating Historical Usages
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "ChangeIsKey/llama3-janus"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+input_text = "1800<|t|>awful<|t|>Used to emphasize something unpleasant or negative; ‘such a’, ‘an absolute’.<|s|>"
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+output = model.generate(**inputs, temperature=1.0, top_p=0.9, max_new_tokens=50)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+For more examples, see the GitHub repository [Historical Word Usage Generation](https://github.com/ChangeIsKey/historical-word-usage-generation)
+
+### Limitations & Ethical Considerations
+- **Historical Bias**: The model may reflect biases present in historical texts.
+- **Time Granularity**: The temporal resolution is approximate (~50 years RMSE).
+- **Modern Influence**: Despite fine-tuning, the model may still generate modern phrases in older contexts.
+- **Not Trained for Fairness**: The model has not been explicitly trained to be fair or unbiased. It may produce sensitive, outdated, or culturally inappropriate content.
+
+### Citation
+If you use Janus, please cite:
+```
+@article{10.1162/tacl_a_00761,
+    author = {Cassotti, Pierluigi and Tahmasebi, Nina},
+    title = {Sense-specific Historical Word Usage Generation},
+    journal = {Transactions of the Association for Computational Linguistics},
+    volume = {13},
+    pages = {690-708},
+    year = {2025},
+    month = {07},
+    abstract = {Large-scale sense-annotated corpora are important for a range of tasks but are hard to come by. Dictionaries that record and describe the vocabulary of a language often offer a small set of real-world example sentences for each sense of a word. However, on their own, these sentences are too few to be used as diachronic sense-annotated corpora. We propose a targeted strategy for training and evaluating generative models producing historically and semantically accurate word usages given any word, sense definition, and year triple. Our results demonstrate that fine-tuned models can generate usages with the same properties as real-world example sentences from a reference dictionary. Thus the generated usages will be suitable for training and testing computational models where large-scale sense-annotated corpora are needed but currently unavailable.},
+    issn = {2307-387X},
+    doi = {10.1162/tacl_a_00761},
+    url = {https://doi.org/10.1162/tacl\_a\_00761},
+    eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00761/2535111/tacl\_a\_00761.pdf},
+}
+```

config.json

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+  "architectures": [
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+  ],
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+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.0",
+  "use_cache": true,
+  "vocab_size": 128260
+}

generation_config.json

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+  "do_sample": true,
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+  "max_length": 4096,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.37.0"
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special_tokens_map.json

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