Image-to-Text
Transformers.js
PyTorch
ONNX
Safetensors
vision-encoder-decoder
image-text-to-text
image-captioning
Instructions to use Mozilla/distilvit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use Mozilla/distilvit with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-to-text', 'Mozilla/distilvit');
Model save
Browse files- README.md +66 -71
- config.json +184 -183
- generation_config.json +5 -5
- metrics.txt +2 -0
- model.safetensors +1 -1
- preprocessor_config.json +36 -22
- training_args.bin +2 -2
README.md
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---
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metrics:
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- rouge
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You can find the code used to create the model here: https://github.com/mozilla/distilvit
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---
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license: apache-2.0
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base_model: mozilla/distilvit
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tags:
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- generated_from_trainer
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metrics:
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- rouge
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model-index:
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- name: distilvit
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# distilvit
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This model is a fine-tuned version of [mozilla/distilvit](https://huggingface.co/mozilla/distilvit) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Gen Len: 10.6487
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- Loss: 0.1739
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- Meteor: 0.4120
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- Rouge1: 50.0916
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- Rouge2: 24.7223
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- Rougel: 46.9416
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- Rougelsum: 46.9372
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 100
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- eval_batch_size: 100
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 1
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### Training results
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| Training Loss | Epoch | Step | Gen Len | Validation Loss | Meteor | Rouge1 | Rouge2 | Rougel | Rougelsum |
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|:-------------:|:------:|:----:|:-------:|:---------------:|:------:|:-------:|:-------:|:-------:|:---------:|
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| No log | 0.3891 | 100 | 10.4163 | 0.1764 | 0.4117 | 50.0198 | 24.6331 | 46.9071 | 46.8907 |
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| No log | 0.7782 | 200 | 10.6487 | 0.1739 | 0.4120 | 50.0916 | 24.7223 | 46.9416 | 46.9372 |
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### Framework versions
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- Transformers 4.40.2
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- Pytorch 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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config.json
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{
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"_name_or_path": "distilvit
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"VisionEncoderDecoderModel"
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"_name_or_path": "distilbert/distilgpt2",
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"GPT2LMHeadModel"
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{
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"_name_or_path": "mozilla/distilvit",
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| 184 |
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generation_config.json
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@@ -1,5 +1,5 @@
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metrics.txt
ADDED
|
@@ -0,0 +1,2 @@
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|
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|
|
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|
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{'eval_loss': 0.17637203633785248, 'eval_rouge1': 50.0198, 'eval_rouge2': 24.6331, 'eval_rougeL': 46.9071, 'eval_rougeLsum': 46.8907, 'eval_meteor': 0.4116637061220643, 'eval_gen_len': 10.41630231105559, 'eval_runtime': 233.9623, 'eval_samples_per_second': 13.686, 'eval_steps_per_second': 0.141, 'epoch': 0.38910505836575876}
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{'eval_loss': 0.17389048635959625, 'eval_rouge1': 50.0916, 'eval_rouge2': 24.7223, 'eval_rougeL': 46.9416, 'eval_rougeLsum': 46.9372, 'eval_meteor': 0.41204454830693554, 'eval_gen_len': 10.648657089319176, 'eval_runtime': 232.7742, 'eval_samples_per_second': 13.756, 'eval_steps_per_second': 0.142, 'epoch': 0.7782101167315175}
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model.safetensors
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preprocessor_config.json
CHANGED
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@@ -1,22 +1,36 @@
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| 2 |
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| 4 |
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| 12 |
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| 15 |
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| 16 |
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| 17 |
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| 3 |
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| 11 |
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training_args.bin
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