Model save
Browse files
README.md
ADDED
|
@@ -0,0 +1,95 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
base_model: google/bigbird-roberta-base
|
| 4 |
+
tags:
|
| 5 |
+
- generated_from_trainer
|
| 6 |
+
model-index:
|
| 7 |
+
- name: bigbird-roberta-base-fineweb-edu-llama3-annotations-4096-vN
|
| 8 |
+
results: []
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 12 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 13 |
+
|
| 14 |
+
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/pszemraj/eduscore-regression/runs/04oc07hx)
|
| 15 |
+
# bigbird-roberta-base-fineweb-edu-llama3-annotations-4096-vN
|
| 16 |
+
|
| 17 |
+
This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on an unknown dataset.
|
| 18 |
+
It achieves the following results on the evaluation set:
|
| 19 |
+
- Loss: 0.2178
|
| 20 |
+
- Mse: 0.2178
|
| 21 |
+
|
| 22 |
+
## Model description
|
| 23 |
+
|
| 24 |
+
More information needed
|
| 25 |
+
|
| 26 |
+
## Intended uses & limitations
|
| 27 |
+
|
| 28 |
+
More information needed
|
| 29 |
+
|
| 30 |
+
## Training and evaluation data
|
| 31 |
+
|
| 32 |
+
More information needed
|
| 33 |
+
|
| 34 |
+
## Training procedure
|
| 35 |
+
|
| 36 |
+
### Training hyperparameters
|
| 37 |
+
|
| 38 |
+
The following hyperparameters were used during training:
|
| 39 |
+
- learning_rate: 1e-05
|
| 40 |
+
- train_batch_size: 4
|
| 41 |
+
- eval_batch_size: 4
|
| 42 |
+
- seed: 90085
|
| 43 |
+
- gradient_accumulation_steps: 32
|
| 44 |
+
- total_train_batch_size: 128
|
| 45 |
+
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-09
|
| 46 |
+
- lr_scheduler_type: linear
|
| 47 |
+
- lr_scheduler_warmup_ratio: 0.05
|
| 48 |
+
- num_epochs: 1.0
|
| 49 |
+
|
| 50 |
+
### Training results
|
| 51 |
+
|
| 52 |
+
| Training Loss | Epoch | Step | Validation Loss | Mse |
|
| 53 |
+
|:-------------:|:------:|:----:|:---------------:|:------:|
|
| 54 |
+
| 0.4763 | 0.0288 | 100 | 0.4468 | 0.4468 |
|
| 55 |
+
| 0.3078 | 0.0577 | 200 | 0.3130 | 0.3130 |
|
| 56 |
+
| 0.3088 | 0.0865 | 300 | 0.2695 | 0.2695 |
|
| 57 |
+
| 0.2379 | 0.1153 | 400 | 0.2618 | 0.2618 |
|
| 58 |
+
| 0.289 | 0.1441 | 500 | 0.2583 | 0.2583 |
|
| 59 |
+
| 0.3049 | 0.1730 | 600 | 0.2723 | 0.2723 |
|
| 60 |
+
| 0.2292 | 0.2018 | 700 | 0.2477 | 0.2477 |
|
| 61 |
+
| 0.2677 | 0.2306 | 800 | 0.2369 | 0.2369 |
|
| 62 |
+
| 0.3181 | 0.2594 | 900 | 0.2307 | 0.2307 |
|
| 63 |
+
| 0.2551 | 0.2883 | 1000 | 0.2411 | 0.2411 |
|
| 64 |
+
| 0.2743 | 0.3171 | 1100 | 0.2350 | 0.2350 |
|
| 65 |
+
| 0.2383 | 0.3459 | 1200 | 0.2424 | 0.2424 |
|
| 66 |
+
| 0.2191 | 0.3747 | 1300 | 0.2279 | 0.2279 |
|
| 67 |
+
| 0.2431 | 0.4036 | 1400 | 0.2232 | 0.2232 |
|
| 68 |
+
| 0.2161 | 0.4324 | 1500 | 0.2307 | 0.2307 |
|
| 69 |
+
| 0.2459 | 0.4612 | 1600 | 0.2246 | 0.2246 |
|
| 70 |
+
| 0.2403 | 0.4900 | 1700 | 0.2232 | 0.2232 |
|
| 71 |
+
| 0.251 | 0.5189 | 1800 | 0.2421 | 0.2421 |
|
| 72 |
+
| 0.2565 | 0.5477 | 1900 | 0.2207 | 0.2207 |
|
| 73 |
+
| 0.2274 | 0.5765 | 2000 | 0.2294 | 0.2294 |
|
| 74 |
+
| 0.2272 | 0.6053 | 2100 | 0.2192 | 0.2192 |
|
| 75 |
+
| 0.2668 | 0.6342 | 2200 | 0.2204 | 0.2204 |
|
| 76 |
+
| 0.2434 | 0.6630 | 2300 | 0.2196 | 0.2196 |
|
| 77 |
+
| 0.2464 | 0.6918 | 2400 | 0.2185 | 0.2185 |
|
| 78 |
+
| 0.2338 | 0.7206 | 2500 | 0.2166 | 0.2166 |
|
| 79 |
+
| 0.243 | 0.7495 | 2600 | 0.2165 | 0.2165 |
|
| 80 |
+
| 0.1891 | 0.7783 | 2700 | 0.2201 | 0.2201 |
|
| 81 |
+
| 0.2355 | 0.8071 | 2800 | 0.2167 | 0.2167 |
|
| 82 |
+
| 0.2231 | 0.8359 | 2900 | 0.2168 | 0.2168 |
|
| 83 |
+
| 0.2274 | 0.8648 | 3000 | 0.2243 | 0.2243 |
|
| 84 |
+
| 0.2287 | 0.8936 | 3100 | 0.2203 | 0.2203 |
|
| 85 |
+
| 0.261 | 0.9224 | 3200 | 0.2186 | 0.2186 |
|
| 86 |
+
| 0.2187 | 0.9512 | 3300 | 0.2176 | 0.2176 |
|
| 87 |
+
| 0.2069 | 0.9801 | 3400 | 0.2178 | 0.2178 |
|
| 88 |
+
|
| 89 |
+
|
| 90 |
+
### Framework versions
|
| 91 |
+
|
| 92 |
+
- Transformers 4.42.3
|
| 93 |
+
- Pytorch 2.3.1+cu121
|
| 94 |
+
- Datasets 2.20.0
|
| 95 |
+
- Tokenizers 0.19.1
|