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Browse files- AIRA_FineTuning.ipynb +0 -0
- Aira_emissions.csv +2 -0
- LICENSE +190 -0
- README.md +141 -0
- added_tokens.json +6 -0
- config.json +39 -0
- generation_config.json +6 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- optimizer.pt +3 -0
- pytorch_model.bin +3 -0
- rng_state.pt +3 -0
- scheduler.pt +3 -0
- special_tokens_map.json +31 -0
- tokenizer_config.json +41 -0
- training_stats.parquet +3 -0
- vocab.json +0 -0
AIRA_FineTuning.ipynb
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Aira_emissions.csv
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timestamp,project_name,run_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue
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2023-09-06T12:21:37,Aira_emissions,9025a722-76d1-4b78-b9fc-1d2616cda561,4692.914520025253,0.25425459270586864,5.4178398438951424e-05,42.5,327.88378425112455,31.305280208587646,0.05540236880083881,0.424876888234572,0.04080152328227049,0.5210807803176817,Singapore,SGP,,,,Linux-5.15.109+-x86_64-with-glibc2.35,3.10.12,2.3.1,12,Intel(R) Xeon(R) CPU @ 2.20GHz,1,1 x NVIDIA A100-SXM4-40GB,103.8547,1.2929,83.48074722290039,machine,N,1.0
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LICENSE
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README.md
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---
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license: apache-2.0
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datasets:
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- nicholasKluge/instruct-aira-dataset
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language:
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- en
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metrics:
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- accuracy
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library_name: transformers
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tags:
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- alignment
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- instruction tuned
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- text generation
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- conversation
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- assistant
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pipeline_tag: text-generation
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widget:
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- text: "<|startofinstruction|>Hello! What is your name?<|endofinstruction|>"
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example_title: Greetings
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- text: "<|startofinstruction|>Can you explain what is Machine Learning?<|endofinstruction|>"
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example_title: Machine Learning
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- text: "<|startofinstruction|>Do you know anything about virtue ethics?<|endofinstruction|>"
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example_title: Ethics
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- text: "<|startofinstruction|>How can I make my girlfriend happy?<|endofinstruction|>"
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example_title: Advise
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inference:
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parameters:
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repetition_penalty: 1.2
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temperature: 0.2
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top_k: 30
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top_p: 0.3
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max_length: 200
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length_penalty: 0.3
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early_stopping: true
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co2_eq_emissions:
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emissions: 0.25
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source: CodeCarbon
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training_type: fine-tuning
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geographical_location: United States of America
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hardware_used: NVIDIA A100-SXM4-40GB
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---
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# Aira-2-124M
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`Aira-2-124M` is the second version of the Aira instruction-tuned series. Aira is an instruction-tuned GPT-style model based on [GPT-2](https://huggingface.co/gpt2). The model was trained with a dataset composed of prompts and completions generated synthetically by prompting already-tuned models (ChatGPT, Llama, Open-Assistant, etc).
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Check our gradio-demo in [Spaces](https://huggingface.co/spaces/nicholasKluge/Aira-Demo).
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## Details
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- **Size:** 124,441,344 parameters
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- **Dataset:** [Instruct-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/instruct-aira-dataset)
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- **Language:** English
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- **Number of Epochs:** 5
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- **Batch size:** 32
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- **Optimizer:** `torch.optim.AdamW` (warmup_steps = 1e2, learning_rate = 5e-4, epsilon = 1e-8)
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- **GPU:** 1 NVIDIA A100-SXM4-40GB
|
57 |
+
- **Emissions:** 0.25 KgCO2 (Singapore)
|
58 |
+
- **Total Energy Consumption:** 0.52 kWh
|
59 |
+
|
60 |
+
This repository has the [notebook](AIRA_FineTuning.ipynb) used to train this model.
|
61 |
+
|
62 |
+
## Usage
|
63 |
+
|
64 |
+
Three special tokens are used to mark the user side of the interaction and the model's response:
|
65 |
+
|
66 |
+
`<|startofinstruction|>`What is a language model?`<|endofinstruction|>`A language model is a probability distribution over a vocabulary.`<|endofcompletion|>`
|
67 |
+
|
68 |
+
```python
|
69 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
|
70 |
+
import torch
|
71 |
+
|
72 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
73 |
+
|
74 |
+
tokenizer = AutoTokenizer.from_pretrained('nicholasKluge/Aira-2-124M')
|
75 |
+
aira = AutoModelForCausalLM.from_pretrained('nicholasKluge/Aira-2-124M')
|
76 |
+
|
77 |
+
aira.eval()
|
78 |
+
aira.to(device)
|
79 |
+
|
80 |
+
question = input("Enter your question: ")
|
81 |
+
|
82 |
+
inputs = tokenizer(tokenizer.bos_token + question + tokenizer.sep_token, return_tensors="pt").to(device)
|
83 |
+
|
84 |
+
responses = aira.generate(**inputs,
|
85 |
+
bos_token_id=tokenizer.bos_token_id,
|
86 |
+
pad_token_id=tokenizer.pad_token_id,
|
87 |
+
eos_token_id=tokenizer.eos_token_id,
|
88 |
+
do_sample=True,
|
89 |
+
top_k=50,
|
90 |
+
max_length=500,
|
91 |
+
top_p=0.95,
|
92 |
+
temperature=0.7,
|
93 |
+
num_return_sequences=2)
|
94 |
+
|
95 |
+
print(f"Question: 👤 {question}\n")
|
96 |
+
|
97 |
+
for i, response in enumerate(responses):
|
98 |
+
print(f'Response {i+1}: 🤖 {tokenizer.decode(response, skip_special_tokens=True).replace(question, "")}')
|
99 |
+
```
|
100 |
+
|
101 |
+
The model will output something like:
|
102 |
+
|
103 |
+
```markdown
|
104 |
+
>>>Question: 👤 What is the capital of Brazil?
|
105 |
+
|
106 |
+
>>>Response 1: 🤖 The capital of Brazil is Brasília.
|
107 |
+
>>>Response 2: 🤖 The capital of Brazil is Brasília.
|
108 |
+
```
|
109 |
+
|
110 |
+
## Limitations
|
111 |
+
|
112 |
+
🤥 Generative models can perpetuate the generation of pseudo-informative content, that is, false information that may appear truthful.
|
113 |
+
|
114 |
+
🤬 In certain types of tasks, generative models can produce harmful and discriminatory content inspired by historical stereotypes.
|
115 |
+
|
116 |
+
## Evaluation
|
117 |
+
|
118 |
+
| Model|Average|[ARC](https://arxiv.org/abs/1803.05457)|[HellaSwag](https://arxiv.org/abs/1905.07830)|[MMLU](https://arxiv.org/abs/2009.03300)|[TruthfulQA](https://arxiv.org/abs/2109.07958)|
|
119 |
+
|---|---|---|---|---|---|
|
120 |
+
| [Aira-2-124M](https://huggingface.co/nicholasKluge/Aira-Instruct-124M) |**30.54**|**24.57**|31.29|25.29|**41.02**|
|
121 |
+
| GPT-2 | 29.99 | 21.84 | **31.6** | **25.86** | 40.67 |
|
122 |
+
|
123 |
+
## Cite as 🤗
|
124 |
+
|
125 |
+
```latex
|
126 |
+
|
127 |
+
@misc{nicholas22aira,
|
128 |
+
doi = {10.5281/zenodo.6989727},
|
129 |
+
url = {https://huggingface.co/nicholasKluge/Aira-2-124M},
|
130 |
+
author = {Nicholas Kluge Corrêa and Carolina Del Pino},
|
131 |
+
title = {Aira},
|
132 |
+
year = {2023},
|
133 |
+
publisher = {HuggingFace},
|
134 |
+
journal = {HuggingFace repository},
|
135 |
+
}
|
136 |
+
|
137 |
+
```
|
138 |
+
|
139 |
+
## License
|
140 |
+
|
141 |
+
The `Aira-2-124M` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.
|
added_tokens.json
ADDED
@@ -0,0 +1,6 @@
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|
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+
{
|
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|
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|
4 |
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"<|pad|>": 50260,
|
5 |
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|
6 |
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}
|
config.json
ADDED
@@ -0,0 +1,39 @@
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|
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{
|
2 |
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|
3 |
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"activation_function": "gelu_new",
|
4 |
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|
5 |
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"GPT2LMHeadModel"
|
6 |
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
26 |
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|
27 |
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"summary_type": "cls_index",
|
28 |
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"summary_use_proj": true,
|
29 |
+
"task_specific_params": {
|
30 |
+
"text-generation": {
|
31 |
+
"do_sample": true,
|
32 |
+
"max_length": 50
|
33 |
+
}
|
34 |
+
},
|
35 |
+
"torch_dtype": "float32",
|
36 |
+
"transformers_version": "4.33.0",
|
37 |
+
"use_cache": true,
|
38 |
+
"vocab_size": 50261
|
39 |
+
}
|
generation_config.json
ADDED
@@ -0,0 +1,6 @@
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|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
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|
4 |
+
"eos_token_id": 50256,
|
5 |
+
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|
6 |
+
}
|
merges.txt
ADDED
The diff for this file is too large to render.
See raw diff
|
|
model.safetensors
ADDED
@@ -0,0 +1,3 @@
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|
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|
1 |
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version https://git-lfs.github.com/spec/v1
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optimizer.pt
ADDED
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version https://git-lfs.github.com/spec/v1
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pytorch_model.bin
ADDED
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rng_state.pt
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special_tokens_map.json
ADDED
@@ -0,0 +1,31 @@
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
tokenizer_config.json
ADDED
@@ -0,0 +1,41 @@
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
33 |
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|
34 |
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|
35 |
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|
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|
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|
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|
39 |
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|
40 |
+
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|
41 |
+
}
|
training_stats.parquet
ADDED
@@ -0,0 +1,3 @@
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|
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version https://git-lfs.github.com/spec/v1
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|
vocab.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|