Salesforce/wikisql
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How to use e22vvb/EN_t5-small_10_wikiSQL with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("e22vvb/EN_t5-small_10_wikiSQL")
model = AutoModelForSeq2SeqLM.from_pretrained("e22vvb/EN_t5-small_10_wikiSQL")This model is a fine-tuned version of t5-small on the wikisql dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|---|---|---|---|---|---|---|
| 0.1933 | 1.0 | 4049 | 0.1549 | 0.7965 | 0.7075 | 0.7423 |
| 0.161 | 2.0 | 8098 | 0.1345 | 0.8123 | 0.7211 | 0.7569 |
| 0.1459 | 3.0 | 12147 | 0.1256 | 0.8165 | 0.7253 | 0.7611 |
| 0.1371 | 4.0 | 16196 | 0.1194 | 0.8236 | 0.7321 | 0.7681 |
| 0.1293 | 5.0 | 20245 | 0.1159 | 0.8275 | 0.7355 | 0.7718 |
| 0.1243 | 6.0 | 24294 | 0.1135 | 0.8283 | 0.7356 | 0.7722 |
| 0.1245 | 7.0 | 28343 | 0.1116 | 0.831 | 0.7383 | 0.7748 |
| 0.1167 | 8.0 | 32392 | 0.1104 | 0.8325 | 0.7401 | 0.7765 |
| 0.1134 | 9.0 | 36441 | 0.1097 | 0.8325 | 0.7408 | 0.7769 |
| 0.1131 | 10.0 | 40490 | 0.1096 | 0.8329 | 0.741 | 0.7772 |
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("e22vvb/EN_t5-small_10_wikiSQL") model = AutoModelForSeq2SeqLM.from_pretrained("e22vvb/EN_t5-small_10_wikiSQL")