📢 If you're interesting in quick application of target sentiment analysis towards your data, you might be insterested in using fine-tuned FlanT5-xl version. Reason is a quick performance: I've added batching support for series of sentiment analysis models in this card:
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📢 If you're interesting in quick application of target sentiment analysis towards your data, you might be insterested in using fine-tuned FlanT5-xl version. Reason is a quick performance: I've added batching support for series of sentiment analysis models in this card:
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https://huggingface.co/collections/nicolay-r/sentiment-analysis-advances-665ba391e0eba729021ea101
nicolay-r/sentiment-analysis-advances-665ba391e0eba729021ea101
collection
resource
https://huggingface.co/collections/nicolay-r/sentiment-analysis-advances-665ba391e0eba729021ea101
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The provider implementation:
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The provider implementation:
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https://github.com/nicolay-r/nlp-thirdgate/blob/master/llm/transformers_flan_t5.py
https://github.com/nicolay-r/nlp-thirdgate/blob/master/llm/transformers_flan_t5.py
link
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📺 How to quick launch: 
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📺 How to quick launch: 
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https://github.com/nicolay-r/bulk-chain/blob/master/test/test_provider_batching.py
https://github.com/nicolay-r/bulk-chain/blob/master/test/test_provider_batching.py
link
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Reason for using? experimenting in out-of domain, the noticed the performance of xl version similar to LLaMA-3-3b-instruct.
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Reason for using? experimenting in out-of domain, the noticed the performance of xl version similar to LLaMA-3-3b-instruct.
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🔑 Key takeaways of adaptaiont:
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🔑 Key takeaways of adaptaiont:
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- paddings and truncation strategies for batching mode:
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- paddings and truncation strategies for batching mode:
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- 
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- 
https://huggingface.co/docs/transformers/en/pad_truncation
https://huggingface.co/docs/transformers/en/pad_truncation
link
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- add_special_tokens=False causes a drastic changes in the result behaviour (FlanT5 models).
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- add_special_tokens=False causes a drastic changes in the result behaviour (FlanT5 models).
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💥 Crashes on pad_token_id=50256 during generation proces.
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💥 Crashes on pad_token_id=50256 during generation proces.
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🔻  use_bf16 mode performs 3 times slower on CPU.
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🔻  use_bf16 mode performs 3 times slower on CPU.
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🚀 Performance for BASE sized model:
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🚀 Performance for BASE sized model:
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https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base
nicolay-r/flan-t5-tsa-thor-base
model
resource
https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base
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17.2 it/s (prompt) and 5.22 it/s (3-step CoT) (CPU Core i5-1140G7)
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17.2 it/s (prompt) and 5.22 it/s (3-step CoT) (CPU Core i5-1140G7)
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There are other domain-oriented models could be launched via the same provider:
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There are other domain-oriented models could be launched via the same provider:
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https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
nicolay-r/flan-t5-emotion-cause-thor-base
model
resource
https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
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Reference: 
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Reference: 
https://github.com/huggingface/transformers/issues/26061
https://github.com/huggingface/transformers/issues/26061
link
📢 If you're interesting in quick application of target sentiment analysis towards your data, you might be insterested in using fine-tuned FlanT5-xl version. Reason is a quick performance: I've added batching support for series of sentiment analysis models in this card:
https://huggingface.co/collections/nicolay-r/sentiment-analysis-advances-665ba391e0eba729021ea101

The provider implementation:
https://github.com/nicolay-r/nlp-thirdgate/blob/master/llm/transformers_flan_t5.py

📺 How to quick launch: 
https://github.com/nicolay-r/bulk-chain/blob/master/test/test_provider_batching.py

Reason for using? experimenting in out-of domain, the noticed the performance of xl version similar to LLaMA-3-3b-instruct.

🔑 Key takeaways of adaptaiont:
- paddings and truncation strategies for batching mode:
- https://huggingface.co/docs/transformers/en/pad_truncation
- add_special_tokens=False causes a drastic changes in the result behaviour (FlanT5 models).
💥 Crashes on pad_token_id=50256 during generation proces.
🔻  use_bf16 mode performs 3 times slower on CPU.

🚀 Performance for BASE sized model:
https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base
17.2 it/s (prompt) and 5.22 it/s (3-step CoT) (CPU Core i5-1140G7)

There are other domain-oriented models could be launched via the same provider:
https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base

Reference: https://github.com/huggingface/transformers/issues/26061
👀
John6666 dantezxcd