add: new best model
Browse files- README.md +42 -0
- config.json +75 -0
- generation_config.json +13 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +15 -0
- tokenizer.json +0 -0
- tokenizer_config.json +15 -0
- vocab.json +0 -0
README.md
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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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- 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/lvwerra/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="romman8//tmp/tmpffgavixx/nlp-lab-2023-seq2seq/R-best-fine-tuned-bart-base-full-ft-reward_short_sentences_and_words-2023-07-13T06-49-08")
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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("romman8//tmp/tmpffgavixx/nlp-lab-2023-seq2seq/R-best-fine-tuned-bart-base-full-ft-reward_short_sentences_and_words-2023-07-13T06-49-08")
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model = AutoModelForCausalLMWithValueHead.from_pretrained("romman8//tmp/tmpffgavixx/nlp-lab-2023-seq2seq/R-best-fine-tuned-bart-base-full-ft-reward_short_sentences_and_words-2023-07-13T06-49-08")
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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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"_name_or_path": "nlp-lab-2023-seq2seq/R-facebook-bart-base-full-ft-without-tum-nlp-german-gpt2_easy-prior-pp-bb5c579f",
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"activation_dropout": 0.1,
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"activation_function": "gelu",
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"add_bias_logits": false,
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"add_final_layer_norm": false,
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"architectures": [
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"BartForConditionalGeneration"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 0,
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"classif_dropout": 0.1,
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"classifier_dropout": 0.0,
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"d_model": 768,
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"decoder_attention_heads": 12,
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"decoder_ffn_dim": 3072,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 6,
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"decoder_start_token_id": 2,
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"dropout": 0.1,
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"early_stopping": true,
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"encoder_attention_heads": 12,
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"encoder_ffn_dim": 3072,
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"encoder_layerdrop": 0.0,
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"encoder_layers": 6,
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"eos_token_id": 2,
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"forced_bos_token_id": 0,
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"forced_eos_token_id": 2,
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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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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_2": 2
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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": "4.30.2",
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"use_cache": true,
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"vocab_size": 50265
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"decoder_start_token_id": 2,
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"early_stopping": true,
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"eos_token_id": 2,
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"forced_bos_token_id": 0,
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"forced_eos_token_id": 2,
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"no_repeat_ngram_size": 3,
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"num_beams": 4,
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"pad_token_id": 1,
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"transformers_version": "4.30.2"
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}
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merges.txt
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:bff7048c411c27b78994117e56063bf4703903260c6194c29bdd9cb86f4803f7
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size 557974885
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special_tokens_map.json
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{
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"bos_token": "<s>",
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"cls_token": "<s>",
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"eos_token": "</s>",
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"mask_token": {
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"content": "<mask>",
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"lstrip": true,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"unk_token": "<unk>"
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}
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tokenizer.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": true,
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"cls_token": "<s>",
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"eos_token": "</s>",
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"errors": "replace",
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"mask_token": "<mask>",
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"model_max_length": 1024,
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"pad_token": "<pad>",
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"sep_token": "</s>",
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"tokenizer_class": "BartTokenizer",
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"trim_offsets": true,
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"unk_token": "<unk>"
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}
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vocab.json
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