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LLM2Vec-Swallow-7b-hf-wikipedia-jp-mntp-unsup-simcse

PEFT

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h-iida commited on Sep 12, 2024

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@@ -3,11 +3,11 @@ base_model: tokyotech-llm/Swallow-7b-hf
 library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
@@ -15,188 +15,117 @@ library_name: peft
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-- **Developed by:** [More Information Needed]
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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 ## Training Details
 ### Training Data
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-### Training Procedure
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-#### Training Hyperparameters
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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 ### Framework versions
-- PEFT 0.11.1

 library_name: peft
 ---
+# Model Info
+This is a model that applies LLM2Vec to Swallow. Only the PEFT Adapter is distributed.
+LLM2Vec is fine-tuned on two tasks: MNTP and SimCSE, and this repository contains the results of applying SimCSE after MNTP.
+For the MNTP Adapter, please refer to [this link](https://huggingface.co/uzabase/LLM2Vec-Llama-2-7b-hf-wikipedia-jp-mntp).
 ## Model Details
 <!-- Provide a longer summary of what this model is. -->
+- **Model type:** PEFT
+- **Language(s) (NLP):** Japanese
+- **License:** Apache2.0
+- **Finetuned from model:** [Swallow-7b-hf](https://huggingface.co/tokyotech-llm/Swallow-7b-hf)
 ### Model Sources [optional]
+- **Repository:**  https://github.com/McGill-NLP/llm2vec
+- **Paper:** https://arxiv.org/abs/2404.05961
+## Usage
+- Please see [original LLM2Vec repo](https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse#usage)
 ## Training Details
 ### Training Data
+- Make Corpus from SimCSE from [Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia)
+- Script for making SimCSE Corpus
+```
+import argparse
+import random
+import re
+from pathlib import Path
+from datasets import load_dataset
+from tqdm import tqdm
+def main(args):
+    random.seed(args.seed)
+    wiki_ds = load_dataset("wikimedia/wikipedia", "20231101.ja")
+    sampled_index = random.sample(range(len(wiki_ds["train"])), args.N)
+    sample_wiki = wiki_ds["train"][sampled_index]
+    output_texts = []
+    for title, text in tqdm(zip(sample_wiki["title"], sample_wiki["text"])):
+        output_texts.append(title)
+        sentences = re.split("[\n。]", text)
+        for sentence in sentences:
+            if len(sentence) > args.min_sentence_len:
+                output_texts.append(sentence.strip()+"。")
+    with args.output_path.open(mode="w") as f:
+        for line in output_texts:
+            f.write(line)
+            f.write("\n")
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--N", default=200000, type=int)
+    parser.add_argument("--seed", default=42, type=int)
+    parser.add_argument("-o", "--output_path", type=Path)
+    parser.add_argument("--min_sentence_len", default=50, type=int)
+    args = parser.parse_args()
+    main(args)
+```
+#### Training Hyperparameter
+- simcse_dropout: 0.3
+- bidirectional: true
+- pooling_mode: "mean"
+- remove_unused_columns: false
+- learning_rate: 3e-5
+- loss_scale: 20
+- batch_size: 256
+- gradient_accumulation_steps: 1
+- max_seq_length: 128
+- lora_r: 16
+- torch_dtype: "bfloat16"
+- attn_implementation: "flash_attention_2"
+- seed: 42
+- bf16: true
+- gradient_checkpointing: true
+#### Accelerator Settings
+- deepspeed_config:
+  - gradient_accumulation_steps: 1
+  - gradient_clipping: 1.0
+  - offload_optimizer_device: nvme
+  - offload_optimizer_nvme_path: /nvme
+  - zero3_save_16bit_model: true
+  - zero_stage: 2
+- distributed_type: DEEPSPEED
+- downcast_bf16: 'no'
+- dynamo_config:
+  - dynamo_backend: INDUCTOR
+  - dynamo_mode: default
+  - dynamo_use_dynamic: true
+  - dynamo_use_fullgraph: true
+- enable_cpu_affinity: false
+- machine_rank: 0
+- main_training_function: main
+- mixed_precision: bf16
+- num_machines: 1
+- num_processes: 2
+- rdzv_backend: static
+- same_network: true
+- quse_cpu: false
 ### Framework versions
+- Python: 3.12.3
+- PEFT 0.11.1
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.0
+- PyTorch: 2.3.0
+- Accelerate: 0.30.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+- MTEB: 1.13.0