pszemraj / textsum Goto Github PK

    from .pytorch import *  # type: ignore[misc]
  File "/home/user/apps/anaconda3/envs/autogptq/lib/python3.10/site-packages/doctr/models/recognition/predictor/pytorch.py", line 14, in <module>
    from ._utils import remap_preds, split_crops
  File "/home/user/apps/anaconda3/envs/autogptq/lib/python3.10/site-packages/doctr/models/recognition/predictor/_utils.py", line 10, in <module>
    from ..utils import merge_multi_strings
  File "/home/user/apps/anaconda3/envs/autogptq/lib/python3.10/site-packages/doctr/models/recognition/utils.py", line 8, in <module>
    from rapidfuzz.string_metric import levenshtein
ModuleNotFoundError: No module named 'rapidfuzz.string_metric'

from textsum.summarize import Summarizer

model_name = "pszemraj/led-large-book-summary"
summarizer = Summarizer(
    model_name_or_path=model_name,  # you can use any Seq2Seq model on the Hub
    token_batch_length=4096,  # tokens to batch summarize at a time, up to 16384
)

lin_reg_txt = """In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression.[1] This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.[2]

In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models.[3] Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of the response given the values of the predictors, rather than on the joint probability distribution of all of these variables, which is the domain of multivariate analysis.

Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively in practical applications.[4] This is because models which depend linearly on their unknown parameters are easier to fit than models which are non-linearly related to their parameters and because the statistical properties of the resulting estimators are easier to determine.

Linear regression has many practical uses. Most applications fall into one of the following two broad categories:

If the goal is error reduction in prediction or forecasting, linear regression can be used to fit a predictive model to an observed data set of values of the response and explanatory variables. After developing such a model, if additional values of the explanatory variables are collected without an accompanying response value, the fitted model can be used to make a prediction of the response.
If the goal is to explain variation in the response variable that can be attributed to variation in the explanatory variables, linear regression analysis can be applied to quantify the strength of the relationship between the response and the explanatory variables, and in particular to determine whether some explanatory variables may have no linear relationship with the response at all, or to identify which subsets of explanatory variables may contain redundant information about the response.
Linear regression models are often fitted using the least squares approach, but they may also be fitted in other ways, such as by minimizing the "lack of fit" in some other norm (as with least absolute deviations regression), or by minimizing a penalized version of the least squares cost function as in ridge regression (L2-norm penalty) and lasso (L1-norm penalty). Use of the Mean Squared Error(MSE) as the cost on a dataset that has many large outliers, can result in a model that fits the outliers more than the true data due to the higher importance assigned by MSE to large errors. So a cost functions that are robust to outliers should be used if the dataset has many large outliers. Conversely, the least squares approach can be used to fit models that are not linear models. Thus, although the terms "least squares" and "linear model" are closely linked, they are not synonymous."""

out_str = summarizer.summarize_string(lin_reg_txt)
print(f"summary: {out_str}")

Generating Summaries: 100%|██████████████████████| 1/1 [09:59<00:00, 599.52s/it]
summary: In. At the end of this chapter is a good way to sum up these types of fun times list of fun. In statistics, by using a variety of ways to make fun of pop quizzes are another kind of fun we like to have. The first kind of "fun" in terms of making fun of late-term wackiness goes into the second kind of use of oohs and wowsers. The third type of "favorites" we see in this review is the sort of non-joke wizzooka variety of wackos who want to be our friends but can't be because we're not giving any away. The fourth kind of ferry wacko is the kind that brings people back to their homes once they've been made or gone. The fifth kind of jayhawk is the shape of the bird, the size of the hole in the moon, etc. And the last one? Well, it's really hard to say since we only ever see the good kind. We'll leave it for you guys to enjoy.

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accelerate==0.22.0
anyio==4.0.0
appnope @ file:///Users/ec2-user/ci_py311/appnope_1678317440516/work
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
asttokens @ file:///opt/conda/conda-bld/asttokens_1646925590279/work
astunparse==1.6.3
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attrs==23.1.0
Babel==2.12.1
backcall @ file:///home/ktietz/src/ci/backcall_1611930011877/work
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bleach==6.0.0
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certifi==2023.7.22
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debugpy @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_30hp2nowkm/croot/debugpy_1690905056188/work
decorator @ file:///opt/conda/conda-bld/decorator_1643638310831/work
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dm-tree==0.1.8
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etils==1.4.1
executing @ file:///opt/conda/conda-bld/executing_1646925071911/work
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fire==0.5.0
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idna==3.4
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ipython @ file:///private/var/folders/c_/qfmhj66j0tn016nkx_th4hxm0000gp/T/abs_41_yzn938_/croot/ipython_1691532095563/work
isoduration==20.11.0
jax==0.4.13
jaxlib==0.4.13
jedi @ file:///Users/ec2-user/ci_py311_2/jedi_1679336327335/work
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jsonschema-specifications==2023.7.1
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jupyter-lsp==2.2.0
jupyter_client @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_5e4bbpqn9e/croot/jupyter_client_1680171866753/work
jupyter_core @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_b82fz_h369/croot/jupyter_core_1679906581737/work
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jupyter_server_terminals==0.4.4
jupyterlab==4.0.5
jupyterlab-pygments==0.2.2
jupyterlab_server==2.24.0
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keras-core==0.1.5
keras-nlp==0.6.1
libclang==16.0.6
Markdown==3.4.4
markdown-it-py==3.0.0
MarkupSafe==2.1.3
matplotlib-inline @ file:///Users/ec2-user/ci_py311/matplotlib-inline_1678317544666/work
mdurl==0.1.2
mistune==3.0.1
ml-dtypes==0.2.0
mpmath==1.3.0
msgpack==1.0.5
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nbclient==0.8.0
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nbformat==5.9.2
nest-asyncio @ file:///Users/ec2-user/ci_py311/nest-asyncio_1678316288195/work
networkx==3.1
nltk==3.8.1
notebook==7.0.3
notebook_shim==0.2.3
numpy==1.25.2
oauthlib==3.2.2
onnx==1.14.1
onnxconverter-common==1.13.0
opt-einsum==3.3.0
optax==0.1.4
orbax-checkpoint==0.3.5
overrides==7.4.0
packaging @ file:///private/var/folders/c_/qfmhj66j0tn016nkx_th4hxm0000gp/T/abs_2algm5p9lp/croot/packaging_1693575178038/work
pandocfilters==1.5.0
parso @ file:///opt/conda/conda-bld/parso_1641458642106/work
pexpect @ file:///tmp/build/80754af9/pexpect_1605563209008/work
pickleshare @ file:///tmp/build/80754af9/pickleshare_1606932040724/work
platformdirs @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_640usff2p5/croot/platformdirs_1692205656061/work
prometheus-client==0.17.1
prompt-toolkit @ file:///Users/ec2-user/ci_py311/prompt-toolkit_1678317707969/work
protobuf==3.20.3
psutil @ file:///Users/ec2-user/ci_py311_2/psutil_1679337242143/work
ptyprocess @ file:///tmp/build/80754af9/ptyprocess_1609355006118/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl
pure-eval @ file:///opt/conda/conda-bld/pure_eval_1646925070566/work
pyasn1==0.5.0
pyasn1-modules==0.3.0
pycparser==2.21
Pygments @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_cflayrfqsi/croot/pygments_1684279981084/work
python-dateutil @ file:///tmp/build/80754af9/python-dateutil_1626374649649/work
python-json-logger==2.0.7
PyYAML==6.0.1
pyzmq @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_e6wiufh44w/croot/pyzmq_1686601381524/work
referencing==0.30.2
regex==2023.8.8
requests==2.31.0
requests-oauthlib==1.3.1
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rich==13.5.2
rpds-py==0.10.0
rsa==4.9
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scipy==1.11.2
Send2Trash==1.8.2
six @ file:///tmp/build/80754af9/six_1644875935023/work
sniffio==1.3.0
soupsieve==2.4.1
stack-data @ file:///opt/conda/conda-bld/stack_data_1646927590127/work
sympy==1.12
tensorboard==2.12.3
tensorboard-data-server==0.7.1
tensorflow==2.12.1
tensorflow-cpu==2.12.1
tensorflow-estimator==2.12.0
tensorflow-hub==0.14.0
tensorflow-io-gcs-filesystem==0.33.0
tensorflow-text==2.12.1
tensorstore==0.1.41
termcolor==2.3.0
terminado==0.17.1
textsum==0.2.0
tf2onnx==1.15.1
tinycss2==1.2.1
tokenizers==0.13.3
toolz==0.12.0
torch==2.0.1
tornado @ file:///private/var/folders/sy/f16zz6x50xz3113nwtb9bvq00000gp/T/abs_aeotsmw12l/croot/tornado_1690848274212/work
tqdm==4.66.1
traitlets @ file:///Users/ec2-user/ci_py311/traitlets_1678316097905/work
transformers==4.32.1
typing_extensions==4.5.0
uri-template==1.3.0
urllib3==1.26.16
wcwidth @ file:///Users/ktietz/demo/mc3/conda-bld/wcwidth_1629357192024/work
webcolors==1.13
webencodings==0.5.1
websocket-client==1.6.2
Werkzeug==2.3.7
wrapt==1.14.1
zipp==3.16.2

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| ID | GPU | MEM |
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|  0 |  5% | 11% |
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