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zephyr-7b-alpha

Text Generation
Transformers
PyTorch
Safetensors
English
mistral
Generated from Trainer
conversational
text-generation-inference
Inference Endpoints
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  1. README.md +147 -0
  2. added_tokens.json +5 -0
  3. all_results.json +21 -0
  4. colab-demo.ipynb +0 -0
  5. config.json +26 -0
  6. eval_results.json +16 -0
  7. generation_config.json +6 -0
  8. gitattributes +35 -0
  9. model-00001-of-00008.safetensors +3 -0
  10. model-00002-of-00008.safetensors +3 -0
  11. model-00003-of-00008.safetensors +3 -0
  12. model-00004-of-00008.safetensors +3 -0
  13. model-00005-of-00008.safetensors +3 -0
  14. model-00006-of-00008.safetensors +3 -0
  15. model-00007-of-00008.safetensors +3 -0
  16. model-00008-of-00008.safetensors +3 -0
  17. model.safetensors.index.json +298 -0
  18. pytorch_model-00001-of-00008.bin +3 -0
  19. pytorch_model-00002-of-00008.bin +3 -0
  20. pytorch_model-00003-of-00008.bin +3 -0
  21. pytorch_model-00004-of-00008.bin +3 -0
  22. pytorch_model-00005-of-00008.bin +3 -0
  23. pytorch_model-00006-of-00008.bin +3 -0
  24. pytorch_model-00007-of-00008.bin +3 -0
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  26. pytorch_model.bin.index.json +298 -0
  27. special_tokens_map.json +11 -0
  28. thumbnail.png +0 -0
  29. tokenizer.json +0 -0
  30. tokenizer.model +3 -0
  31. tokenizer_config.json +46 -0
  32. train_results.json +8 -0
  33. trainer_state.json +0 -0
  34. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: zephyr-7b-alpha
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+ results: []
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+ license: mit
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+ datasets:
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+ - stingning/ultrachat
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+ - openbmb/UltraFeedback
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+ language:
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+ - en
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+ base_model: mistralai/Mistral-7B-v0.1
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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+
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+
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+ # Model Card for Zephyr 7B Alpha
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+
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+ Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-α is the first model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so.
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+
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+
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+ ## Model description
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+
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+ - **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
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+ - **Language(s) (NLP):** Primarily English
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+ - **License:** MIT
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+ - **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
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+
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+ ### Model Sources
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** https://github.com/huggingface/alignment-handbook
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+ - **Demo:** https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
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+
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+ ## Intended uses & limitations
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+
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+ The model was initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities.
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+
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+ Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
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+
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+ ```python
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+ # Install transformers from source - only needed for versions <= v4.34
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+ # pip install git+https://github.com/huggingface/transformers.git
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+ # pip install accelerate
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+
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+ import torch
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+ from transformers import pipeline
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+
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+ pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha", torch_dtype=torch.bfloat16, device_map="auto")
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+
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+ # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
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+ messages = [
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+ {
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+ "role": "system",
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+ "content": "You are a friendly chatbot who always responds in the style of a pirate",
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+ },
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+ {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
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+ ]
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+ prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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+ print(outputs[0]["generated_text"])
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+ # <|system|>
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+ # You are a friendly chatbot who always responds in the style of a pirate.</s>
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+ # <|user|>
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+ # How many helicopters can a human eat in one sitting?</s>
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+ # <|assistant|>
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+ # Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!
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+ ```
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ Zephyr-7B-α has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
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+ It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
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+
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+
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+ ## Training and evaluation data
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+
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+ Zephyr 7B Alpha achieves the following results on the evaluation set:
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+
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+ - Loss: 0.4605
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+ - Rewards/chosen: -0.5053
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+ - Rewards/rejected: -1.8752
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+ - Rewards/accuracies: 0.7812
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+ - Rewards/margins: 1.3699
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+ - Logps/rejected: -327.4286
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+ - Logps/chosen: -297.1040
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+ - Logits/rejected: -2.7153
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+ - Logits/chosen: -2.7447
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+
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+ - learning_rate: 5e-07
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+ - train_batch_size: 2
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - distributed_type: multi-GPU
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+ - num_devices: 16
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+ - total_train_batch_size: 32
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+ - total_eval_batch_size: 64
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
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+ | 0.5602 | 0.05 | 100 | 0.5589 | -0.3359 | -0.8168 | 0.7188 | 0.4809 | -306.2607 | -293.7161 | -2.6554 | -2.6797 |
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+ | 0.4852 | 0.1 | 200 | 0.5136 | -0.5310 | -1.4994 | 0.8125 | 0.9684 | -319.9124 | -297.6181 | -2.5762 | -2.5957 |
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+ | 0.5212 | 0.15 | 300 | 0.5168 | -0.1686 | -1.1760 | 0.7812 | 1.0074 | -313.4444 | -290.3699 | -2.6865 | -2.7125 |
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+ | 0.5496 | 0.21 | 400 | 0.4835 | -0.1617 | -1.7170 | 0.8281 | 1.5552 | -324.2635 | -290.2326 | -2.7947 | -2.8218 |
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+ | 0.5209 | 0.26 | 500 | 0.5054 | -0.4778 | -1.6604 | 0.7344 | 1.1826 | -323.1325 | -296.5546 | -2.8388 | -2.8667 |
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+ | 0.4617 | 0.31 | 600 | 0.4910 | -0.3738 | -1.5180 | 0.7656 | 1.1442 | -320.2848 | -294.4741 | -2.8234 | -2.8521 |
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+ | 0.4452 | 0.36 | 700 | 0.4838 | -0.4591 | -1.6576 | 0.7031 | 1.1986 | -323.0770 | -296.1796 | -2.7401 | -2.7653 |
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+ | 0.4674 | 0.41 | 800 | 0.5077 | -0.5692 | -1.8659 | 0.7656 | 1.2967 | -327.2416 | -298.3818 | -2.6740 | -2.6945 |
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+ | 0.4656 | 0.46 | 900 | 0.4927 | -0.5279 | -1.6614 | 0.7656 | 1.1335 | -323.1518 | -297.5553 | -2.7817 | -2.8015 |
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+ | 0.4102 | 0.52 | 1000 | 0.4772 | -0.5767 | -2.0667 | 0.7656 | 1.4900 | -331.2578 | -298.5311 | -2.7160 | -2.7455 |
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+ | 0.4663 | 0.57 | 1100 | 0.4740 | -0.8038 | -2.1018 | 0.7656 | 1.2980 | -331.9604 | -303.0741 | -2.6994 | -2.7257 |
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+ | 0.4737 | 0.62 | 1200 | 0.4716 | -0.3783 | -1.7015 | 0.7969 | 1.3232 | -323.9545 | -294.5634 | -2.6842 | -2.7135 |
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+ | 0.4259 | 0.67 | 1300 | 0.4866 | -0.6239 | -1.9703 | 0.7812 | 1.3464 | -329.3312 | -299.4761 | -2.7046 | -2.7356 |
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+ | 0.4935 | 0.72 | 1400 | 0.4747 | -0.5626 | -1.7600 | 0.7812 | 1.1974 | -325.1243 | -298.2491 | -2.7153 | -2.7444 |
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+ | 0.4211 | 0.77 | 1500 | 0.4645 | -0.6099 | -1.9993 | 0.7656 | 1.3894 | -329.9109 | -299.1959 | -2.6944 | -2.7236 |
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+ | 0.4931 | 0.83 | 1600 | 0.4684 | -0.6798 | -2.1082 | 0.7656 | 1.4285 | -332.0890 | -300.5934 | -2.7006 | -2.7305 |
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+ | 0.5029 | 0.88 | 1700 | 0.4595 | -0.5063 | -1.8951 | 0.7812 | 1.3889 | -327.8267 | -297.1233 | -2.7108 | -2.7403 |
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+ | 0.4965 | 0.93 | 1800 | 0.4613 | -0.5561 | -1.9079 | 0.7812 | 1.3518 | -328.0831 | -298.1203 | -2.7226 | -2.7523 |
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+ | 0.4337 | 0.98 | 1900 | 0.4608 | -0.5066 | -1.8718 | 0.7656 | 1.3652 | -327.3599 | -297.1296 | -2.7175 | -2.7469 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.34.0
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+ - Pytorch 2.0.1+cu118
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+ - Datasets 2.12.0
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+ - Tokenizers 0.14.0
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