Push model using huggingface_hub.
Browse files- README.md +27 -55
- config.json +46 -72
- model.safetensors +2 -2
README.md
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---
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license: apache-2.0
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base_model: facebook/bart-base
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tags:
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- name: billsum_2052_bart-base
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results: []
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---
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should probably proofread and complete it, then remove this comment. -->
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It achieves the following results on the evaluation set:
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- Loss: 2.4857
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- Rouge1: 0.151
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- Rouge2: 0.0596
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- Rougel: 0.123
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- Rougelsum: 0.1301
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- Gen Len: 20.0
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 16
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- total_train_batch_size: 64
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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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- lr_scheduler_warmup_steps: 500
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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
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| No log | 1.69 | 500 | 2.4571 | 0.1623 | 0.0709 | 0.1353 | 0.1414 | 20.0 |
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| No log | 3.38 | 1000 | 2.4533 | 0.1564 | 0.0638 | 0.1273 | 0.1345 | 20.0 |
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| No log | 5.07 | 1500 | 2.4592 | 0.149 | 0.0586 | 0.1216 | 0.1287 | 20.0 |
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| 1.6068 | 6.75 | 2000 | 2.4967 | 0.1487 | 0.0588 | 0.122 | 0.1286 | 20.0 |
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| 1.6068 | 8.44 | 2500 | 2.4857 | 0.151 | 0.0596 | 0.123 | 0.1301 | 20.0 |
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### Framework versions
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- Transformers 4.38.2
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- Pytorch 2.0.0+cu117
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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---
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license: apache-2.0
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tags:
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- trl
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- ppo
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- transformers
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- reinforcement-learning
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---
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# TRL Model
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This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
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guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
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## Usage
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To use this model for inference, first install the TRL library:
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```bash
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python -m pip install trl
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```
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You can then generate text as follows:
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```python
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from transformers import pipeline
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generator = pipeline("text-generation", model="baek26//tmp/tmpks0nb0mn/baek26/billsum_2052_bart-base")
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outputs = generator("Hello, my llama is cute")
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```
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If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
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```python
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from transformers import AutoTokenizer
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from trl import AutoModelForCausalLMWithValueHead
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tokenizer = AutoTokenizer.from_pretrained("baek26//tmp/tmpks0nb0mn/baek26/billsum_2052_bart-base")
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model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmpks0nb0mn/baek26/billsum_2052_bart-base")
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inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
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outputs = model(**inputs, labels=inputs["input_ids"])
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```
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config.json
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{
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"early_stopping": true,
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"gradient_checkpointing": false,
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1",
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"2": "LABEL_2"
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},
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"init_std": 0.02,
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"is_encoder_decoder": true,
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"
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"max_position_embeddings": 1024,
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"model_type": "bart",
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"no_repeat_ngram_size": 3,
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"normalize_before": false,
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"normalize_embedding": true,
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"num_beams": 4,
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"num_hidden_layers": 6,
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"pad_token_id": 1,
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"scale_embedding": false,
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"task_specific_params": {
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"summarization": {
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"length_penalty": 1.0,
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"max_length": 128,
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"min_length": 12,
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"num_beams": 4
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},
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"summarization_cnn": {
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"length_penalty": 2.0,
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"max_length": 142,
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"min_length": 56,
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"num_beams": 4
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},
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"summarization_xsum": {
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"length_penalty": 1.0,
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"max_length": 62,
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"min_length": 11,
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"num_beams": 6
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.38.2",
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"use_cache": true,
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"vocab_size": 50265
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}
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{
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"exp_name": "rlqaf",
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"seed": 0,
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"log_with": null,
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"task_name": null,
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"model_name": "facebook/bart-base",
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"query_dataset": "imdb",
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"reward_model": "sentiment-analysis:lvwerra/distilbert-imdb",
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"remove_unused_columns": true,
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"tracker_kwargs": {},
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"accelerator_kwargs": {},
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"project_kwargs": {},
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"tracker_project_name": "trl",
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"push_to_hub_if_best_kwargs": {},
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"steps": 20000,
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"learning_rate": 1.41e-06,
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"adap_kl_ctrl": true,
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"init_kl_coef": 0.2,
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"kl_penalty": "kl",
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"target": 6,
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"horizon": 10000,
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"gamma": 0.9,
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"lam": 0.95,
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"cliprange": 0.2,
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"cliprange_value": 0.2,
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"vf_coef": 0.1,
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"batch_size": 1,
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"forward_batch_size": null,
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"mini_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"world_size": 1,
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"ppo_epochs": 4,
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"max_grad_norm": null,
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"optimize_cuda_cache": null,
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"optimize_device_cache": false,
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"early_stopping": true,
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"target_kl": 1,
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"compare_steps": 1,
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"ratio_threshold": 10.0,
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"use_score_scaling": false,
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"use_score_norm": false,
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"score_clip": null,
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"whiten_rewards": false,
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"is_encoder_decoder": true,
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"is_peft_model": false,
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"backward_batch_size": 1,
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"global_backward_batch_size": 1,
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"global_batch_size": 1
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:7f4153290468a6d6b34c0eb88831fc5a48e436a1a9f0b5c374e7333cbe11cda3
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size 557915872
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