Model save

README.md

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 ---
-library_name: transformers
-tags: []
+license: mit
+base_model: gpt2
+tags:
+- generated_from_trainer
+model-index:
+- name: art-gpt2-base
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-### Downstream Use [optional]
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure 
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-## Citation [optional]
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-## Glossary [optional]
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+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# art-gpt2-base
+
+This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 6.0920
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 32
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 100
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 6.6246        | 3.23  | 100  | 6.0652          |
+| 5.5673        | 6.45  | 200  | 5.5941          |
+| 4.8544        | 9.68  | 300  | 5.2210          |
+| 4.1301        | 12.9  | 400  | 4.9281          |
+| 3.4252        | 16.13 | 500  | 4.8584          |
+| 2.8133        | 19.35 | 600  | 4.8369          |
+| 2.2897        | 22.58 | 700  | 4.8968          |
+| 1.8635        | 25.81 | 800  | 5.0623          |
+| 1.4989        | 29.03 | 900  | 5.1647          |
+| 1.1677        | 32.26 | 1000 | 5.3719          |
+| 0.9198        | 35.48 | 1100 | 5.4282          |
+| 0.7353        | 38.71 | 1200 | 5.6292          |
+| 0.6025        | 41.94 | 1300 | 5.6874          |
+| 0.5122        | 45.16 | 1400 | 5.7219          |
+| 0.432         | 48.39 | 1500 | 5.8266          |
+| 0.3801        | 51.61 | 1600 | 5.8598          |
+| 0.3457        | 54.84 | 1700 | 5.9109          |
+| 0.3131        | 58.06 | 1800 | 5.9386          |
+| 0.2904        | 61.29 | 1900 | 5.9634          |
+| 0.265         | 64.52 | 2000 | 5.9652          |
+| 0.2526        | 67.74 | 2100 | 5.9944          |
+| 0.2363        | 70.97 | 2200 | 6.0083          |
+| 0.2276        | 74.19 | 2300 | 6.0417          |
+| 0.2155        | 77.42 | 2400 | 6.0281          |
+| 0.2083        | 80.65 | 2500 | 6.0560          |
+| 0.2056        | 83.87 | 2600 | 6.0612          |
+| 0.2008        | 87.1  | 2700 | 6.0770          |
+| 0.1958        | 90.32 | 2800 | 6.0843          |
+| 0.192         | 93.55 | 2900 | 6.0831          |
+| 0.1889        | 96.77 | 3000 | 6.0930          |
+| 0.1881        | 100.0 | 3100 | 6.0920          |
+
+
+### Framework versions
+
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.38.2"
+}

model.safetensors

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 size 372329088

runs/Apr15_05-10-14_eb47771529ab/events.out.tfevents.1713157815.eb47771529ab.8520.0

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