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+ ---
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+ base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
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+ library_name: peft
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+ datasets:
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+ - manupinasco/simple_sentences
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+ - manupinasco/syntax_analysis
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+ language:
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+ - en
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+ ---
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+
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+ # Fine-Tuned LLaMA 3.1 on Dependency Parsing
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+
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+ This model is a fine-tuned version of **LLaMA 3.1** specifically designed to automate dependency parsing of simple sentences, categorizing words into their syntactic roles according to Universal Dependency Parsing tags.
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ The model has been fine-tuned to accurately parse simple sentences by classifying each word into its respective dependency category, such as `nsubj`, `obj`, and `root`, following the Universal Dependency framework. This fine-tuning enhances the LLaMA 3.1 model's ability to understand and analyze sentence structures, making it a valuable tool for linguistic analysis and natural language processing tasks.
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+
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+ - **Developed by:** Emanuel Pinasco
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+ - **Model type:** NLP, Dependency Parsing
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+ - **Language(s) (NLP):** English
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+
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+ ### Direct Use
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+
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+ The model can be used directly for syntactic analysis and linguistic research, where dependency parsing is required to understand sentence structures. It’s particularly suited for tasks involving simple sentence parsing.
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+
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+ ### Recommended Prompt
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+
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+ "Below is a sentence. Please, provide the dependency category for each word."
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+ {sentence}
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+
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+ ### Downstream Use [optional]
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+
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+ Ideal for integration into larger NLP systems that require detailed sentence parsing, such as grammar checking tools, machine translation systems, and educational software.
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+
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+ ### Out-of-Scope Use
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+
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+ The model is not designed for complex sentence structures, idiomatic expressions, or languages other than English. Misuse may involve attempts to apply it to tasks beyond simple dependency parsing, leading to inaccurate results.
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+
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+ ### Recommendations
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+
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+ Users (both direct and downstream) should be aware that the model's accuracy may decline with more complex or less conventional sentence structures. It's recommended to use this model in conjunction with other tools for more comprehensive linguistic analysis.
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+
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+
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+ ## Training Details
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+
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+ The model was trained on a curated dataset of simple English sentences annotated with Universal Dependency Parsing tags. The dataset was sourced from the "manupinasco/syntax_analysis" dataset available on Hugging Face's Datasets Hub. The training data focused on ensuring high accuracy in syntactic role assignment, aiming to improve the model's ability to understand and generate syntactically correct responses.
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+
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+ ### Training Data
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+
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+ The model was trained on a curated dataset of simple English sentences annotated with Universal Dependency Parsing tags. The training data focused on ensuring high accuracy in syntactic role assignment.
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+
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+ ### Training Procedure
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+
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+ The training procedure involved fine-tuning the unsloth/Meta-Llama-3.1-8B-Instruct model using a custom prompt format inspired by the Alpaca prompt template. The procedure included quantization to 4-bit to reduce memory usage, and mixed precision training to leverage GPU capabilities effectively.
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+
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+ Key components of the training process:
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+
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+ Model Quantization: 4-bit quantization was applied to the model to reduce VRAM usage while maintaining performance.
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+ Gradient Checkpointing: Enabled using "unsloth" mode to save memory during training, which allowed handling longer sequences effectively.
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+ Prompt Template: The model was trained using a structured prompt that provided instructions and expected responses, ensuring consistency and clarity in the tasks presented to the model.
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+
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+ #### Training Hyperparameters
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+
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+ - **Batch Size**: 2 per device
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+ - **Gradient Accumulation**: 4 steps
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+ - **Warmup Steps**: 5
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+ - **Max Training Steps**: 60
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+ - **Learning Rate**: 2e-4
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+ - **Optimizer**: AdamW with 8-bit quantization
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+ - **Weight Decay**: 0.01
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+ - **LR Scheduler**: Linear
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+ - **Mixed Precision**: fp16 (or bf16 if supported)
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+ -
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+ ## Evaluation
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+
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+ The model's performance was evaluated using the test split from the same dataset. The evaluation focused on syntactic role assignment accuracy.
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+
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+ #### Testing Data
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+
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+ The model was evaluated using a separate dataset of simple sentences annotated with Universal Dependency tags.
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+
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+ #### Factors
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+
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+ Evaluation focused on sentence simplicity, vocabulary diversity, and syntactic structure variations.
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+
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+ #### Metrics
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+
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+ Accuracy in word classification into dependency categories was the primary metric.
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+
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+ #### Summary
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+
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+ The fine-tuned model demonstrates high accuracy in dependency parsing of simple English sentences, making it a robust tool for basic syntactic analysis.
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+
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+ ## Model Card Authors
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+
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+ Emanuel Pinasco
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+
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+ ## Model Card Contact
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+
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+ Emanuel Pinasco
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+
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+ ### Framework versions
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+
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+ - PEFT 0.12.0
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+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 131072,
2061
+ "pad_token": "<|finetune_right_pad_id|>",
2062
+ "padding_side": "right",
2063
+ "tokenizer_class": "PreTrainedTokenizerFast"
2064
+ }
manupinasco__syntax-analysis-llama-3.1/training_llama_3_1.py ADDED
@@ -0,0 +1,232 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ """Training Llama 3.1.ipynb
3
+
4
+ Automatically generated by Colab.
5
+
6
+ Original file is located at
7
+ https://colab.research.google.com/drive/19LthnXISqvXgzE-1S2crf-PtTv3OaRmo
8
+
9
+ # **TRAINING DEL MODELO**
10
+
11
+ **Instalación de dependencias**
12
+ """
13
+
14
+ # Commented out IPython magic to ensure Python compatibility.
15
+ # %%capture
16
+ # !pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
17
+ # !pip install --no-deps "xformers<0.0.27" "trl<0.9.0" peft accelerate bitsandbytes
18
+ # !pip install datasets # Se instalan
19
+
20
+ from unsloth import FastLanguageModel # Normalmente se utiliza transformers, pero esta es una librería que permite finetunear rápidamente modelos de lenguaje
21
+ import torch
22
+ max_seq_length = 2048 # Se puede elegir cualquier largo. Esta librería permite autoscaling (escala automáticamente si el dataset cuenta con un máximo mayor)
23
+ dtype = None
24
+ load_in_4bit = True # Cuantificación 4bit para reducir el uso de memoria
25
+
26
+ model, tokenizer = FastLanguageModel.from_pretrained(
27
+ model_name = "unsloth/Meta-Llama-3.1-8B-Instruct", # Modelo Llama 3.1 pre-entrenado para la respuesta a instrucciones
28
+ max_seq_length = max_seq_length,
29
+ dtype = dtype,
30
+ load_in_4bit = load_in_4bit,
31
+ )
32
+
33
+ """**Definición de los Lora Adapters**"""
34
+
35
+ model = FastLanguageModel.get_peft_model(
36
+ model,
37
+ r = 16, # Configura el número de parámetros de rango para LoRA. Se recomienda usar valores como 8, 16, 32, 64, 128.
38
+ target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
39
+ "gate_proj", "up_proj", "down_proj",],
40
+ lora_alpha = 16, # Establece el valor de alpha para LoRA. Es un hiperparámetro que controla la intensidad de la adaptación de LoRA.
41
+ lora_dropout = 0, # Configura la tasa de abandono para LoRA. Se puede usar cualquier valor, pero 0 es la configuración más optimizada.
42
+ bias = "none", # Determina el tipo de sesgo para LoRA. La opción "none" es la más optimizada y elimina el sesgo en el modelo.
43
+ # [NUEVO] La opción "unsloth" reduce el uso de VRAM en un 30% y permite tamaños de lote hasta 2 veces mayores.
44
+ use_gradient_checkpointing = "unsloth", # Usa True o "unsloth" para habilitar el registro de puntos de control de gradientes, lo que es útil para contextos muy largos.
45
+ random_state = 3407, # Establece la semilla para la generación de números aleatorios, asegurando reproducibilidad en el entrenamiento.
46
+ use_rslora = False, # Indica si se utiliza LoRA con rango estabilizado, que puede mejorar la estabilidad del entrenamiento.
47
+ loftq_config = None, # Configura LoftQ si se requiere. LoftQ es una técnica adicional que puede ser utilizada en el modelo.
48
+ )
49
+
50
+ """**Preparación del dataset**"""
51
+
52
+ alpaca_prompt = """Below is an instruction that describes a task, with an input that gives more context. Write a response that appropriately completes the request.
53
+
54
+ ### Instruction:
55
+ Below you have a sentence in quotation marks. Provide the syntactic category of each word in the context of the sentence.
56
+
57
+ ### Sentence:
58
+ "{}"
59
+
60
+ ### Response:
61
+ {}"""
62
+
63
+ EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
64
+ def formatting_prompts_func(examples):
65
+ length= len(examples["sentence"])
66
+ sentences = examples["sentence"]
67
+ tags = examples["sentence_tagged"]
68
+ texts = []
69
+ for sentence,tag in zip(sentences,tags):
70
+ # Must add EOS_TOKEN, otherwise your generation will go on forever!
71
+ text = alpaca_prompt.format(sentence,tag) + EOS_TOKEN
72
+ texts.append(text)
73
+ return { "text" : texts, }
74
+ pass
75
+
76
+ from datasets import load_dataset
77
+ dataset = load_dataset("manupinasco/syntax_analysis")
78
+ dataset_train = dataset["train"].map(formatting_prompts_func, batched = True,)
79
+ dataset_test = dataset["test"]
80
+
81
+ """**Prueba pre-entrenamiento**"""
82
+
83
+ FastLanguageModel.for_inference(model) # Enable native 2x faster inference
84
+ for sentence in dataset_test["sentence"][:5]:
85
+ inputs = tokenizer(
86
+ [
87
+ alpaca_prompt.format(
88
+ "Below you have a sentence in quotation marks. Provide the syntactic category of each word in the context of the sentence.", # instruction
89
+ f' "{sentence}" ', # input
90
+ "", # output - leave this blank for generation!
91
+ )
92
+ ], return_tensors = "pt").to("cuda")
93
+
94
+ outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
95
+ response = str(tokenizer.batch_decode(outputs))
96
+ response=response.split("Response:")[1].replace("']", "").replace("\\n", "").replace("<|eot_id|>", "").lstrip()
97
+ print("INPUT: "+sentence+"/// RESPONSE: "+response)
98
+
99
+ """**Testeo pre-entrenamiento**"""
100
+
101
+ total = len(dataset_train["sentence"])
102
+ correct = 0
103
+ i=0
104
+ for sentence in dataset_test["sentence"]:
105
+ inputs = tokenizer(
106
+ [
107
+ alpaca_prompt.format(
108
+ "Below you have a sentence in quotation marks. Provide the syntactic category of each word in the context of the sentence.", # instruction
109
+ f' "{sentence}" ', # input
110
+ "", # output - leave this blank for generation!
111
+ )
112
+ ], return_tensors = "pt").to("cuda")
113
+
114
+ outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
115
+ response = str(tokenizer.batch_decode(outputs))
116
+ response=response.split("Response:")[1].replace("']", "").replace("\\n", "").replace("<|eot_id|>", "").lstrip()
117
+ if response.lower()==dataset_test["sentence_tagged"][i].lower():
118
+ correct+=1
119
+ print("RESPONSE: "+response)
120
+ print("CORRECT_RESPONSE: "+dataset_test["sentence_tagged"][i])
121
+ print("CORRECT RESPONSES SO FAR: "+correct)
122
+ print("NUMBER OF SENTENCE: "+i)
123
+ i+=1
124
+
125
+ print("CORRECT "+correct+" OUT OF "+total+". PERCENTAGE "+(correct/total)*100)
126
+
127
+ """**Entrenamiento del modelo**
128
+
129
+
130
+
131
+ * *Epoch*: cantidad de veces que recorre el dataset completo
132
+ * *Batch*: cantidad de subgrupos en los que divide al dataset.
133
+ * Entrenamiento común: cada vez que se recorre un batch, se updatean los weights.
134
+ * Entrenamiento por **gradient accumulation**: para casos donde se cuente con poca memoria. Sirve para ir acumulando el gradiente de las distintas partes del batch de forma tal de no computar el gradiente recién al finalizarlo.
135
+
136
+ * En el caso de gradient accumulation, el batch size es = batch size per device x gradient accumulation steps.
137
+
138
+ * *Batch size*: partes en las que realmente dividí al conjunto de datos.
139
+
140
+ * *Batch size per device*: partes en las que dividí al conjunto de datos para, al terminar de recorrer cada una de estas partes, hacer un update de los weights.
141
+
142
+ * *Gradient accumulation steps*: veces que, por cada batch size per device, acumulé las gradientes previo al update de los weights.
143
+
144
+
145
+
146
+
147
+
148
+
149
+
150
+
151
+
152
+
153
+ """
154
+
155
+ from trl import SFTTrainer
156
+ from transformers import TrainingArguments
157
+ from unsloth import is_bfloat16_supported
158
+
159
+ trainer = SFTTrainer(
160
+ model = model,
161
+ tokenizer = tokenizer,
162
+ train_dataset = dataset_train,
163
+ dataset_text_field = "text",
164
+ max_seq_length = max_seq_length,
165
+ dataset_num_proc = 2,
166
+ packing = False, # Puede hacer el entrenamiento 5x más rápido para oraciones breves.
167
+ args = TrainingArguments(
168
+ per_device_train_batch_size = 2,
169
+ gradient_accumulation_steps = 4,
170
+ warmup_steps = 5,
171
+ # num_train_epochs = 1, # Si se setea a 1 hace una corrida completa por todo el dataset.
172
+ max_steps = 60,
173
+ learning_rate = 2e-4,
174
+ fp16 = not is_bfloat16_supported(),
175
+ bf16 = is_bfloat16_supported(),
176
+ logging_steps = 1,
177
+ optim = "adamw_8bit",
178
+ weight_decay = 0.01,
179
+ lr_scheduler_type = "linear",
180
+ seed = 3407,
181
+ output_dir = "outputs",
182
+ ),
183
+ )
184
+
185
+ trainer_stats = trainer.train()
186
+
187
+ """# **TESTEO DEL MODELO**
188
+
189
+ **Prueba post-entrenamiento**
190
+ """
191
+
192
+ FastLanguageModel.for_inference(model) # Enable native 2x faster inference
193
+ for sentence in dataset_test["sentence"][:5]:
194
+ inputs = tokenizer(
195
+ [
196
+ alpaca_prompt.format(
197
+ f' "{sentence}" ', # input
198
+ "", # output - leave this blank for generation!
199
+ )
200
+ ], return_tensors = "pt").to("cuda")
201
+
202
+ outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
203
+ response = str(tokenizer.batch_decode(outputs))
204
+ response=response.split("Response:")[1].replace("']", "").replace("\\n", "").replace("<|eot_id|>", "").lstrip()
205
+ print("INPUT: "+sentence+"/// RESPONSE: "+response)
206
+
207
+ """**Testeo post-entrenamiento**"""
208
+
209
+ total = len(dataset_train["sentence"])
210
+ correct = 0
211
+ i=0
212
+ for sentence in dataset_test["sentence"]:
213
+ inputs = tokenizer(
214
+ [
215
+ alpaca_prompt.format(
216
+ f' "{sentence}" ', # input
217
+ "", # output - leave this blank for generation!
218
+ )
219
+ ], return_tensors = "pt").to("cuda")
220
+
221
+ outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
222
+ response = str(tokenizer.batch_decode(outputs))
223
+ response=response.split("Response:")[1].replace("']", "").replace("\\n", "").replace("<|eot_id|>", "").lstrip()
224
+ if response.lower()==dataset_test["sentence_tagged"][i].lower():
225
+ correct+=1
226
+ print("RESPONSE: "+response)
227
+ print("CORRECT_RESPONSE: "+dataset_test["sentence_tagged"][i])
228
+ print("CORRECT RESPONSES SO FAR: "+correct)
229
+ print("NUMBER OF SENTENCE: "+i)
230
+ i+=1
231
+
232
+ print("CORRECT "+correct+" OUT OF "+total+". PERCENTAGE "+(correct/total)*100)