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Model: AuricErgeson/Antelope-textTosql Source: Original Platform
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---
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library_name: transformers
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tags:
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- text-to-sql
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- sql
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- natural-language-processing
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- peft
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- qlora
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- phi-2
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- sqlite
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- database
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- code-generation
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- question-answering
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language:
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- en
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license: mit
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base_model: microsoft/phi-2
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datasets:
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- spider
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pipeline_tag: text-generation
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---
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# 🦌 Antelope Text-to-SQL
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**Convert plain English questions into SQL queries instantly.**
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Lightweight, fast, and runs on CPU. No database expertise needed.
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🚀 **[Try the live demo →](https://huggingface.co/spaces/AuricErgeson/Antelope-textTosql-demo)**
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---
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## What it does
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| Question | Database | Output |
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|---|---|---|
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| How many employees are there? | company | `SELECT COUNT(*) FROM employees` |
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| List all customers from Germany | store | `SELECT * FROM customers WHERE country = 'Germany'` |
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| What is the average salary by department? | hr | `SELECT department, AVG(salary) FROM employees GROUP BY department` |
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---
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## Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "AuricErgeson/Antelope-textTosql"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True
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)
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def generate_sql(question, db=""):
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prompt = (
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f"### Task: Convert question to SQL. Use only what the question asks. Simple questions need simple SQL.\n"
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f"### Database: {db}\n"
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f"### Question: {question}\n"
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f"### SQL:"
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(
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**inputs,
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max_new_tokens=128,
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temperature=0.1,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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decoded = tokenizer.decode(out[0], skip_special_tokens=True)
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return decoded.split("### SQL:")[-1].strip().split("\n")[0]
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print(generate_sql("How many employees are in each department?", db="company"))
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# → SELECT department, COUNT(*) FROM employees GROUP BY department
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```
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---
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## Why use this model?
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- ✅ **Small** — 2.7B params, runs on modest hardware
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- ✅ **Fast** — CPU inference possible, GPU recommended
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- ✅ **Open** — MIT license, use anywhere
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- ✅ **No setup** — works out of the box with `transformers`
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- ✅ **Cross-domain** — trained on 200+ database schemas
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---
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## Prompt Format
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```
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### Task: Convert question to SQL. Use only what the question asks. Simple questions need simple SQL.
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### Database: {your_database_name}
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### Question: {your_question}
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### SQL:
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```
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---
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## Via HuggingFace Inference API
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```python
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import requests
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API_URL = "https://api-inference.huggingface.co/models/AuricErgeson/Antelope-textTosql"
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headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
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response = requests.post(API_URL, headers=headers, json={
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"inputs": "### Task: Convert question to SQL. Use only what the question asks. Simple questions need simple SQL.\n### Database: company\n### Question: How many employees?\n### SQL:"
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})
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print(response.json())
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```
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---
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## Training Details
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| Property | Value |
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|---|---|
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| Base model | microsoft/phi-2 (2.7B) |
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| Method | QLoRA (4-bit + LoRA) |
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| Dataset | Spider (7,000+ examples, 200+ databases) |
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| LoRA rank | 16 |
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| LoRA alpha | 32 |
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| Target modules | q_proj, v_proj |
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| Learning rate | 2e-4 |
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| Epochs | 3 |
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| Hardware | NVIDIA A100 (Google Colab) |
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| Training time | ~1.5 hours |
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| Adapter size | ~21MB |
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| Merged model size | ~5.56GB |
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---
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## Limitations
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- Not recommended for production databases without output validation
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- Complex multi-join queries may be inaccurate
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- Does not infer table/column names — provide your database name for best results
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---
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## Roadmap
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- [x] Text-to-SQL (this model)
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- [ ] Text-to-Regex (coming soon)
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- [ ] Text-to-Shell (planned)
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---
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## Author
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**Auric Ergeson Nitonde**
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📧 auricergesonnitonde@gmail.com
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🤗 [HuggingFace Profile](https://huggingface.co/AuricErgeson)
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*If this model helped you, consider leaving a like ⭐ — it helps others find it.*
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"_name_or_path": "microsoft/phi-2",
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"architectures": [
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"PhiForCausalLM"
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],
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"attention_dropout": 0.0,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "phi",
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.41.2",
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"use_cache": true,
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"vocab_size": 51200
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}
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"model.layers.6.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.input_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.mlp.fc1.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.mlp.fc1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.mlp.fc2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.mlp.fc2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.dense.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.dense.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
100648
tokenizer.json
Normal file
100648
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
325
tokenizer_config.json
Normal file
325
tokenizer_config.json
Normal file
@@ -0,0 +1,325 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"50256": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"50257": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50258": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50259": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50260": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50261": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50262": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50263": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50264": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50265": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50266": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50267": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50268": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50269": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50270": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50271": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50272": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50273": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50274": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50275": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50276": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50277": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50278": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50279": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50280": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50281": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50282": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50283": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50284": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50285": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50286": {
|
||||||
|
"content": " ",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50287": {
|
||||||
|
"content": "\t\t\t\t\t\t\t\t\t",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50288": {
|
||||||
|
"content": "\t\t\t\t\t\t\t\t",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50289": {
|
||||||
|
"content": "\t\t\t\t\t\t\t",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50290": {
|
||||||
|
"content": "\t\t\t\t\t\t",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50291": {
|
||||||
|
"content": "\t\t\t\t\t",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50292": {
|
||||||
|
"content": "\t\t\t\t",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50293": {
|
||||||
|
"content": "\t\t\t",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"50294": {
|
||||||
|
"content": "\t\t",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"bos_token": "<|endoftext|>",
|
||||||
|
"clean_up_tokenization_spaces": true,
|
||||||
|
"eos_token": "<|endoftext|>",
|
||||||
|
"model_max_length": 2048,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"return_token_type_ids": false,
|
||||||
|
"tokenizer_class": "CodeGenTokenizer",
|
||||||
|
"unk_token": "<|endoftext|>"
|
||||||
|
}
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user