Other
Transformers
Safetensors
PEFT
English
t5
text2text-generation
text-to-sql
sqlite
encoder-decoder
codet5
codet5-plus
lora
schema-aware
spider
natural-language-to-sql
Eval Results (legacy)
text-generation-inference
Instructions to use anmol-unitmole/schema-aware-text-to-sql-codet5p-770m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anmol-unitmole/schema-aware-text-to-sql-codet5p-770m with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("anmol-unitmole/schema-aware-text-to-sql-codet5p-770m") model = AutoModelForSeq2SeqLM.from_pretrained("anmol-unitmole/schema-aware-text-to-sql-codet5p-770m", device_map="auto") - PEFT
How to use anmol-unitmole/schema-aware-text-to-sql-codet5p-770m with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 780 Bytes
ba2fc8c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"architectures": [
"T5ForConditionalGeneration"
],
"bos_token_id": 1,
"classifier_dropout": 0.0,
"d_ff": 4096,
"d_kv": 64,
"d_model": 1024,
"decoder_start_token_id": 0,
"dense_act_fn": "relu",
"dropout_rate": 0.1,
"dtype": "bfloat16",
"eos_token_id": 2,
"feed_forward_proj": "relu",
"initializer_factor": 1.0,
"is_encoder_decoder": true,
"is_gated_act": false,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"n_positions": 512,
"num_decoder_layers": 24,
"num_heads": 16,
"num_layers": 24,
"output_past": true,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"transformers_version": "4.57.6",
"use_cache": true,
"vocab_size": 32100
}
|