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use huggingface bert/xlnet to do sentiment analysis on sst5 and amazon
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| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Latest commitHistory12 Commits | ||||
| .ipynb_checkpoints | .ipynb_checkpoints | |||
| data | data | |||
| model | model | |||
| README.md | README.md | |||
| train.ipynb | train.ipynb | |||
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- README
Model
- Bert: https://hackmd.io/@h5v7rKYIT4GGOr9b0g0k7g/SyKjPsasv
- XLNet: https://hackmd.io/@h5v7rKYIT4GGOr9b0g0k7g/SyKjPsasv
- RoBerta: https://hackmd.io/@h5v7rKYIT4GGOr9b0g0k7g/H1osvVOnv
Dataset
- amazon clawer
- sst5 : https://github.com/prrao87/fine-grained-sentiment/tree/master/data/sst
- amazon 5 Electronics (to balance data): https://jmcauley.ucsd.edu/data/amazon/
Performance
| 0 -- f1 | 1 -- f1 | 2 -- f1 | |
|---|---|---|---|
| BERT | 0.87 | 0.47 | 0.93 |
| XLNet | 0.89 | 0.51 | 0.94 |
| RoBERTa(sum) | 0.86 | 0.43 | 0.91 |
| RoBERTa(pool) | 0.89 | 0.48 | 0.93 |
case study
- A: Not as extreme as touted. (3/5)
- B: IT WAS DIFFERENT TYPE MOVIE BUT IT WAS GOOD. SUSPENSEFUL. (5/5)
- C: Not impressed with this movie, too many unknown variables. (1/5)
- D: The first half is outstanding. Lupita Nyongo is excellent as always -- shes a moody complex character which makes complete sense after the big reveal.Once that big reveal happens, suspending disbelief is difficult, even for a horror movie. It is almost as if they came up with the main premise, than spit-balled an explanation. The explanation / back story is so absurd it requires way too much exposition. Heres hoping Peele gets more grounded in his third act. (3/5)
| A | B | C | D | |
|---|---|---|---|---|
| BERT | 0 | 2 | 0 | 2 |
| XLNet | 1 | 2 | 1 | 1 |
| RoBERTa(sum) | 1 | 1 | 0 | 2 |
| RoBERTa(pool) | 1 | 1 | 0 | 1 |
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use huggingface bert/xlnet to do sentiment analysis on sst5 and amazon
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jupyter-notebook pytorch bert huggingface xlnetResources
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