Super-squash branch 'main' using huggingface_hub

Co-authored-by: SFconvertbot <SFconvertbot@users.noreply.huggingface.co>
Co-authored-by: autoevaluator <autoevaluator@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,28 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text 
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+model.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,263 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- summarization
+- pegasus
+datasets:
+- kmfoda/booksum
+metrics:
+- rouge
+widget:
+- text: large earthquakes along a given fault segment do not occur at random intervals
+    because it takes time to accumulate the strain energy for the rupture. The rates
+    at which tectonic plates move and accumulate strain at their boundaries are approximately
+    uniform. Therefore, in first approximation, one may expect that large ruptures
+    of the same fault segment will occur at approximately constant time intervals.
+    If subsequent main shocks have different amounts of slip across the fault, then
+    the recurrence time may vary, and the basic idea of periodic mainshocks must be
+    modified. For great plate boundary ruptures the length and slip often vary by
+    a factor of 2. Along the southern segment of the San Andreas fault the recurrence
+    interval is 145 years with variations of several decades. The smaller the standard
+    deviation of the average recurrence interval, the more specific could be the long
+    term prediction of a future mainshock.
+  example_title: earthquakes
+- text: ' A typical feed-forward neural field algorithm. Spatiotemporal coordinates
+    are fed into a neural network that predicts values in the reconstructed domain.
+    Then, this domain is mapped to the sensor domain where sensor measurements are
+    available as supervision. Class and Section Problems Addressed Generalization
+    (Section 2) Inverse problems, ill-posed problems, editability; symmetries. Hybrid
+    Representations (Section 3) Computation & memory efficiency, representation capacity,
+    editability: Forward Maps (Section 4) Inverse problems Network Architecture (Section
+    5) Spectral bias, integration & derivatives. Manipulating Neural Fields (Section
+    6) Edit ability, constraints, regularization. Table 2: The five classes of techniques
+    in the neural field toolbox each addresses problems that arise in learning, inference,
+    and control. (Section 3). We can supervise reconstruction via differentiable forward
+    maps that transform Or project our domain (e.g, 3D reconstruction via 2D images;
+    Section 4) With appropriate network architecture choices, we can overcome neural
+    network spectral biases (blurriness) and efficiently compute derivatives and integrals
+    (Section 5). Finally, we can manipulate neural fields to add constraints and regularizations,
+    and to achieve editable representations (Section 6). Collectively, these classes
+    constitute a ''toolbox'' of techniques to help solve problems with neural fields
+    There are three components in a conditional neural field: (1) An encoder or inference
+    function € that outputs the conditioning latent variable 2 given an observation
+    0 E(0) =2. 2 is typically a low-dimensional vector, and is often referred to aS
+    a latent code Or feature code_ (2) A mapping function 4 between Z and neural field
+    parameters O: Y(z) = O; (3) The neural field itself $. The encoder € finds the
+    most probable z given the observations O: argmaxz P(2/0). The decoder maximizes
+    the inverse conditional probability to find the most probable 0 given Z: arg-
+    max P(Olz). We discuss different encoding schemes with different optimality guarantees
+    (Section 2.1.1), both global and local conditioning (Section 2.1.2), and different
+    mapping functions Y (Section 2.1.3) 2. Generalization Suppose we wish to estimate
+    a plausible 3D surface shape given a partial or noisy point cloud. We need a suitable
+    prior over the sur- face in its reconstruction domain to generalize to the partial
+    observations. A neural network expresses a prior via the function space of its
+    architecture and parameters 0, and generalization is influenced by the inductive
+    bias of this function space (Section 5).'
+  example_title: scientific paper
+- text: ' the big variety of data coming from diverse sources is one of the key properties
+    of the big data phenomenon. It is, therefore, beneficial to understand how data
+    is generated in various environments and scenarios, before looking at what should
+    be done with this data and how to design the best possible architecture to accomplish
+    this The evolution of IT architectures, described in Chapter 2, means that the
+    data is no longer processed by a few big monolith systems, but rather by a group
+    of services In parallel to the processing layer, the underlying data storage has
+    also changed and became more distributed This, in turn, required a significant
+    paradigm shift as the traditional approach to transactions (ACID) could no longer
+    be supported. On top of this, cloud computing is becoming a major approach with
+    the benefits of reducing costs and providing on-demand scalability but at the
+    same time introducing concerns about privacy, data ownership, etc In the meantime
+    the Internet continues its exponential growth: Every day both structured and unstructured
+    data is published and available for processing: To achieve competitive advantage
+    companies have to relate their corporate resources to external services, e.g.
+    financial markets, weather forecasts, social media, etc While several of the sites
+    provide some sort of API to access the data in a more orderly fashion; countless
+    sources require advanced web mining and Natural Language Processing (NLP) processing
+    techniques: Advances in science push researchers to construct new instruments
+    for observing the universe O conducting experiments to understand even better
+    the laws of physics and other domains. Every year humans have at their disposal
+    new telescopes, space probes, particle accelerators, etc These instruments generate
+    huge streams of data, which need to be stored and analyzed. The constant drive
+    for efficiency in the industry motivates the introduction of new automation techniques
+    and process optimization: This could not be done without analyzing the precise
+    data that describe these processes. As more and more human tasks are automated,
+    machines provide rich data sets, which can be analyzed in real-time to drive efficiency
+    to new levels. Finally, it is now evident that the growth of the Internet of Things
+    is becoming a major source of data. More and more of the devices are equipped
+    with significant computational power and can generate a continuous data stream
+    from their sensors. In the subsequent sections of this chapter, we will look at
+    the domains described above to see what they generate in terms of data sets. We
+    will compare the volumes but will also look at what is characteristic and important
+    from their respective points of view. 3.1 The Internet is undoubtedly the largest
+    database ever created by humans. While several well described; cleaned, and structured
+    data sets have been made available through this medium, most of the resources
+    are of an ambiguous, unstructured, incomplete or even erroneous nature. Still,
+    several examples in the areas such as opinion mining, social media analysis, e-governance,
+    etc, clearly show the potential lying in these resources. Those who can successfully
+    mine and interpret the Internet data can gain unique insight and competitive advantage
+    in their business An important area of data analytics on the edge of corporate
+    IT and the Internet is Web Analytics.'
+  example_title: data science textbook
+- text: 'Transformer-based models have shown to be very useful for many NLP tasks.
+    However, a major limitation of transformers-based models is its O(n^2)O(n 2) time
+    & memory complexity (where nn is sequence length). Hence, it''s computationally
+    very expensive to apply transformer-based models on long sequences n > 512n>512.
+    Several recent papers, e.g. Longformer, Performer, Reformer, Clustered attention
+    try to remedy this problem by approximating the full attention matrix. You can
+    checkout 🤗''s recent blog post in case you are unfamiliar with these models.
+
+    BigBird (introduced in paper) is one of such recent models to address this issue.
+    BigBird relies on block sparse attention instead of normal attention (i.e. BERT''s
+    attention) and can handle sequences up to a length of 4096 at a much lower computational
+    cost compared to BERT. It has achieved SOTA on various tasks involving very long
+    sequences such as long documents summarization, question-answering with long contexts.
+
+    BigBird RoBERTa-like model is now available in 🤗Transformers. The goal of this
+    post is to give the reader an in-depth understanding of big bird implementation
+    & ease one''s life in using BigBird with 🤗Transformers. But, before going into
+    more depth, it is important to remember that the BigBird''s attention is an approximation
+    of BERT''s full attention and therefore does not strive to be better than BERT''s
+    full attention, but rather to be more efficient. It simply allows to apply transformer-based
+    models to much longer sequences since BERT''s quadratic memory requirement quickly
+    becomes unbearable. Simply put, if we would have ∞ compute & ∞ time, BERT''s attention
+    would be preferred over block sparse attention (which we are going to discuss
+    in this post).
+
+    If you wonder why we need more compute when working with longer sequences, this
+    blog post is just right for you!
+
+    Some of the main questions one might have when working with standard BERT-like
+    attention include:
+
+    Do all tokens really have to attend to all other tokens? Why not compute attention
+    only over important tokens? How to decide what tokens are important? How to attend
+    to just a few tokens in a very efficient way? In this blog post, we will try to
+    answer those questions.
+
+    What tokens should be attended to? We will give a practical example of how attention
+    works by considering the sentence ''BigBird is now available in HuggingFace for
+    extractive question answering''. In BERT-like attention, every word would simply
+    attend to all other tokens.
+
+    Let''s think about a sensible choice of key tokens that a queried token actually
+    only should attend to by writing some pseudo-code. Will will assume that the token
+    available is queried and build a sensible list of key tokens to attend to.
+
+    >>> # let''s consider following sentence as an example >>> example = [''BigBird'',
+    ''is'', ''now'', ''available'', ''in'', ''HuggingFace'', ''for'', ''extractive'',
+    ''question'', ''answering'']
+
+    >>> # further let''s assume, we''re trying to understand the representation of
+    ''available'' i.e. >>> query_token = ''available'' >>> # We will initialize an
+    empty `set` and fill up the tokens of our interest as we proceed in this section.
+    >>> key_tokens = [] # => currently ''available'' token doesn''t have anything
+    to attend Nearby tokens should be important because, in a sentence (sequence of
+    words), the current word is highly dependent on neighboring past & future tokens.
+    This intuition is the idea behind the concept of sliding attention.'
+  example_title: bigbird blog intro
+inference:
+  parameters:
+    max_length: 64
+    no_repeat_ngram_size: 2
+    encoder_no_repeat_ngram_size: 3
+    repetition_penalty: 2.4
+    length_penalty: 0.5
+    num_beams: 4
+    early_stopping: true
+model-index:
+- name: pszemraj/pegasus-large-summary-explain
+  results:
+  - task:
+      type: summarization
+      name: Summarization
+    dataset:
+      name: kmfoda/booksum
+      type: kmfoda/booksum
+      config: kmfoda--booksum
+      split: test
+    metrics:
+    - type: rouge
+      value: 29.1023
+      name: ROUGE-1
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTFhNjg4YTFlODU5MmVjNGVmNDRmMjQ4M2YyZGNmMWRlYjBhZmVhMTY3ZTUxNDkzNjY0OGVmNWJlNmY1OTkzNCIsInZlcnNpb24iOjF9.E_rVKqB7WEerLeRq6JIVTLZ1TgmsThFQJVKh11WH1qWa-cL3766psPWDKe8mK3lNkjmwbiDW0DZlDt4dm2ATCA
+    - type: rouge
+      value: 6.2441
+      name: ROUGE-2
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNDVmZmFlOTgwN2Q3ZWRkZGVkMzU1ZDRkYzU1MWMzMTk1NDM5YTU0MzFjNDljNmZlY2I2NjZmZjcyYjBkZGExZCIsInZlcnNpb24iOjF9.QnuGoMWX8cq5_ukRtiaLRLau_F9XiCjg313GC7Iu1VGK8Kj_9lzU43377VsH0fBWooA1zJjtIK0UA-YpGQQOAA
+    - type: rouge
+      value: 14.7503
+      name: ROUGE-L
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzJhNzE0YjZiZWQ4NDE1Yjg3ZGJjY2ZmYWEwYzU5MTRhYWNiNTcyODU1NzM5NTZhNjNlNmYwNDVlYmZmYjkxOCIsInZlcnNpb24iOjF9.m5BLUMefXa1KivIIE9-gYKYq5aRRbfpQWazqzXxfCsqqp38Lt0ymk6OwXSlQyB_5oksNHIDFKpJX4wjYx2i7Bw
+    - type: rouge
+      value: 27.2375
+      name: ROUGE-LSUM
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTY1OTIxMzBkMGJiZmNiNjZjYmQ2MjUwMjBkYTg5Zjc1NjVlZjllNTg0MDM1NTdhZDJlZmIwOTczOGNkZDc5YyIsInZlcnNpb24iOjF9.bThI16mvqhEuGBhdao0w8j03vv9G9Quy-ITRZzalr41zOour9it4oxEPFCvmPf-nLCQkqgWKUDEzgr6Ww8qgBg
+    - type: loss
+      value: 2.979011058807373
+      name: loss
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOGM0NzM3YTI4Njg4NDY0ZjQzNTZmYTIxYzcxNDBlNzAwNTAxNDE4MTZjYmZmNzYwODU0OWQ1ZjM5YjRmMmFkZiIsInZlcnNpb24iOjF9.EPEP53AoqHz0rjVGStJI2dM7ivxFmOj572I3llWdAoejm3zO1Iq5WDArYsqOse_oLxYCgcqPmNVc5IcLW9x7Dg
+    - type: gen_len
+      value: 467.269
+      name: gen_len
+      verified: true
+      verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjgzYzU2ZjkwN2RhNzJlZmQyZTBlYmUxMTZhNzg0ODMwMjA3OTUzNTIwOWFkZWVmNjVmMTJiZmZhNWFmY2UzZCIsInZlcnNpb24iOjF9.RW5tzk2fcc_m4bgaSopRDFhSR9R8hRaYKrstXH4X5iGP_Xwvhy5Q7-igd2ACnlxIfmtdTmMxLMsvHr5oAZEwDg
+---
+
+
+# pszemraj/pegasus-large-summary-explain
+
+This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on the [booksum](https://github.com/salesforce/booksum) dataset for four total epochs.
+
+It achieves the following results on the evaluation set:
+- eval_loss: 1.1193
+- eval_runtime: 6.6754
+- eval_samples_per_second: 27.714
+- eval_steps_per_second: 1.798
+- epoch: 3.0
+- step: 900
+
+A 1-epoch checkpoint can be found at [pszemraj/pegasus-large-book-summary](https://huggingface.co/pszemraj/pegasus-large-book-summary), which is where the second training session started from.
+
+## Model description
+
+- After some initial tests, it was found that models trained on the [booksum](https://github.com/salesforce/booksum) dataset seem to inherit the summaries' SparkNotes-style explanations; so the user gets a shorter and easier-to-understand version of the text instead of **just** more compact. 
+ - This quality (anecdotally) is favourable for learning/comprehension because summarization datasets that simply make the information more compact (* cough * arXiv) can be so dense that the overall time spent trying to _comprehend_ what it is saying can be the same as just reading the original material.
+
+
+## Intended uses & limitations
+
+- standard pegasus has a max input length of 1024 tokens, therefore the model only saw the first 1024 tokens of a chapter when training, and learned to try to make the chapter's summary from that. Keep this in mind when using this model, as information at the end of a text sequence longer than 1024 tokens may be excluded from the final summary/the model will be biased towards information presented first.
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 4e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.03
+- num_epochs: 4
+
+### Framework versions
+
+- Transformers 4.16.2
+- Pytorch 1.10.2+cu113
+- Datasets 1.18.3
+- Tokenizers 0.11.0

config.json

@@ -0,0 +1,127 @@
+{
+  "_name_or_path": "pszemraj/pegasus-large-book-summary",
+  "activation_dropout": 0.1,
+  "activation_function": "relu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": true,
+  "architectures": [
+    "PegasusForConditionalGeneration"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 0,
+  "classif_dropout": 0.0,
+  "classifier_dropout": 0.0,
+  "d_model": 1024,
+  "decoder_attention_heads": 16,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 16,
+  "decoder_start_token_id": 0,
+  "dropout": 0.1,
+  "early_stopping": true,
+  "encoder_attention_heads": 16,
+  "encoder_ffn_dim": 4096,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 16,
+  "eos_token_id": 1,
+  "extra_pos_embeddings": 1,
+  "force_bos_token_to_be_generated": false,
+  "forced_eos_token_id": 1,
+  "gradient_checkpointing": false,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "length_penalty": 3.5,
+  "max_length": 512,
+  "max_position_embeddings": 1024,
+  "min_length": 32,
+  "model_type": "pegasus",
+  "no_repeat_ngram_size": 3,
+  "normalize_before": true,
+  "normalize_embedding": false,
+  "num_beams": 5,
+  "num_hidden_layers": 16,
+  "pad_token_id": 0,
+  "scale_embedding": true,
+  "static_position_embeddings": true,
+  "task_specific_params": {
+    "summarization_aeslc": {
+      "length_penalty": 0.6,
+      "max_length": 32,
+      "max_position_embeddings": 512
+    },
+    "summarization_arxiv": {
+      "length_penalty": 0.8,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_big_patent": {
+      "length_penalty": 0.7,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_billsum": {
+      "length_penalty": 0.6,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_cnn_dailymail": {
+      "length_penalty": 0.8,
+      "max_length": 128,
+      "max_position_embeddings": 1024
+    },
+    "summarization_gigaword": {
+      "length_penalty": 0.6,
+      "max_length": 32,
+      "max_position_embeddings": 128
+    },
+    "summarization_large": {
+      "length_penalty": 0.8,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_multi_news": {
+      "length_penalty": 0.8,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_newsroom": {
+      "length_penalty": 0.8,
+      "max_length": 128,
+      "max_position_embeddings": 512
+    },
+    "summarization_pubmed": {
+      "length_penalty": 0.8,
+      "max_length": 256,
+      "max_position_embeddings": 1024
+    },
+    "summarization_reddit_tifu": {
+      "length_penalty": 0.6,
+      "max_length": 128,
+      "max_position_embeddings": 512
+    },
+    "summarization_wikihow": {
+      "length_penalty": 0.6,
+      "max_length": 256,
+      "max_position_embeddings": 512
+    },
+    "summarization_xsum": {
+      "length_penalty": 0.8,
+      "max_length": 64,
+      "max_position_embeddings": 512
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.16.2",
+  "use_cache": false,
+  "vocab_size": 96103
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d14b4b1a9bbdc179564a007282f80eac0bbe8491e5f4b5f65ca26851da1f432
+size 2275264008

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7e1cad802995443c74eaaf8ec20755e0d015a6b1e8a564b6b71d0b85a4f4419
+size 2275268647

special_tokens_map.json

@@ -0,0 +1 @@
+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "mask_token": "<mask_2>", "additional_special_tokens": ["<mask_1>", "<unk_2>", "<unk_3>", "<unk_4>", "<unk_5>", "<unk_6>", "<unk_7>", "<unk_8>", "<unk_9>", "<unk_10>", "<unk_11>", "<unk_12>", "<unk_13>", "<unk_14>", "<unk_15>", "<unk_16>", "<unk_17>", "<unk_18>", "<unk_19>", "<unk_20>", "<unk_21>", "<unk_22>", "<unk_23>", "<unk_24>", "<unk_25>", "<unk_26>", "<unk_27>", "<unk_28>", "<unk_29>", "<unk_30>", "<unk_31>", "<unk_32>", "<unk_33>", "<unk_34>", "<unk_35>", "<unk_36>", "<unk_37>", "<unk_38>", "<unk_39>", "<unk_40>", "<unk_41>", "<unk_42>", "<unk_43>", "<unk_44>", "<unk_45>", "<unk_46>", "<unk_47>", "<unk_48>", "<unk_49>", "<unk_50>", "<unk_51>", "<unk_52>", "<unk_53>", "<unk_54>", "<unk_55>", "<unk_56>", "<unk_57>", "<unk_58>", "<unk_59>", "<unk_60>", "<unk_61>", "<unk_62>", "<unk_63>", "<unk_64>", "<unk_65>", "<unk_66>", "<unk_67>", "<unk_68>", "<unk_69>", "<unk_70>", "<unk_71>", "<unk_72>", "<unk_73>", "<unk_74>", "<unk_75>", "<unk_76>", "<unk_77>", "<unk_78>", "<unk_79>", "<unk_80>", "<unk_81>", "<unk_82>", "<unk_83>", "<unk_84>", "<unk_85>", "<unk_86>", "<unk_87>", "<unk_88>", "<unk_89>", "<unk_90>", "<unk_91>", "<unk_92>", "<unk_93>", "<unk_94>", "<unk_95>", "<unk_96>", "<unk_97>", "<unk_98>", "<unk_99>", "<unk_100>", "<unk_101>", "<unk_102>"]}

spiece.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0015189ef36359283fec8b93cf6d9ce51bca37eb1101defc68a53b394913b96c
+size 1912529

tokenizer_config.json

@@ -0,0 +1 @@
+{"pad_token": "<pad>", "eos_token": "</s>", "unk_token": "<unk>", "mask_token": "<mask_2>", "mask_token_sent": "<mask_1>", "offset": 103, "additional_special_tokens": ["<mask_1>", "<unk_2>", "<unk_3>", "<unk_4>", "<unk_5>", "<unk_6>", "<unk_7>", "<unk_8>", "<unk_9>", "<unk_10>", "<unk_11>", "<unk_12>", "<unk_13>", "<unk_14>", "<unk_15>", "<unk_16>", "<unk_17>", "<unk_18>", "<unk_19>", "<unk_20>", "<unk_21>", "<unk_22>", "<unk_23>", "<unk_24>", "<unk_25>", "<unk_26>", "<unk_27>", "<unk_28>", "<unk_29>", "<unk_30>", "<unk_31>", "<unk_32>", "<unk_33>", "<unk_34>", "<unk_35>", "<unk_36>", "<unk_37>", "<unk_38>", "<unk_39>", "<unk_40>", "<unk_41>", "<unk_42>", "<unk_43>", "<unk_44>", "<unk_45>", "<unk_46>", "<unk_47>", "<unk_48>", "<unk_49>", "<unk_50>", "<unk_51>", "<unk_52>", "<unk_53>", "<unk_54>", "<unk_55>", "<unk_56>", "<unk_57>", "<unk_58>", "<unk_59>", "<unk_60>", "<unk_61>", "<unk_62>", "<unk_63>", "<unk_64>", "<unk_65>", "<unk_66>", "<unk_67>", "<unk_68>", "<unk_69>", "<unk_70>", "<unk_71>", "<unk_72>", "<unk_73>", "<unk_74>", "<unk_75>", "<unk_76>", "<unk_77>", "<unk_78>", "<unk_79>", "<unk_80>", "<unk_81>", "<unk_82>", "<unk_83>", "<unk_84>", "<unk_85>", "<unk_86>", "<unk_87>", "<unk_88>", "<unk_89>", "<unk_90>", "<unk_91>", "<unk_92>", "<unk_93>", "<unk_94>", "<unk_95>", "<unk_96>", "<unk_97>", "<unk_98>", "<unk_99>", "<unk_100>", "<unk_101>", "<unk_102>"], "model_max_length": 1024, "special_tokens_map_file": null, "full_tokenizer_file": null, "name_or_path": "pszemraj/pegasus-large-book-summary", "sp_model_kwargs": {}, "tokenizer_class": "PegasusTokenizer"}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95d737d31c01d3e5267f63e8a2c4c2f74120914f368a49bab29c8090e141b2ac
+size 4207