Fix CI with change of name of nlp

.circleci/config.yml

@@ -77,7 +77,7 @@ jobs:
                       - v0.3-torch_and_tf-{{ checksum "setup.py" }}
                       - v0.3-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install git+https://github.com/huggingface/nlp
+            - run: pip install git+https://github.com/huggingface/datasets
             - run: pip install .[sklearn,tf-cpu,torch,testing]
             - run: pip install codecov pytest-cov
             - save_cache:
@@ -104,7 +104,7 @@ jobs:
                       - v0.3-torch-{{ checksum "setup.py" }}
                       - v0.3-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install git+https://github.com/huggingface/nlp
+            - run: pip install git+https://github.com/huggingface/datasets
             - run: pip install .[sklearn,torch,testing]
             - save_cache:
                   key: v0.3-torch-{{ checksum "setup.py" }}
@@ -129,7 +129,7 @@ jobs:
                       - v0.3-tf-{{ checksum "setup.py" }}
                       - v0.3-{{ checksum "setup.py" }}
             - run: pip install --upgrade pip
-            - run: pip install git+https://github.com/huggingface/nlp
+            - run: pip install git+https://github.com/huggingface/datasets
             - run: pip install .[sklearn,tf-cpu,testing]
             - save_cache:
                   key: v0.3-tf-{{ checksum "setup.py" }}

.github/workflows/self-push.yml

@@ -46,7 +46,7 @@ jobs:
         pip install --upgrade pip
         pip install torch!=1.6.0
         pip install .[sklearn,testing,onnxruntime]
-        pip install git+https://github.com/huggingface/nlp
+        pip install git+https://github.com/huggingface/datasets
 
     - name: Are GPUs recognized by our DL frameworks
       run: |

.github/workflows/self-scheduled.yml

@@ -43,7 +43,7 @@ jobs:
         pip install --upgrade pip
         pip install torch!=1.6.0
         pip install .[sklearn,testing,onnxruntime]
-        pip install git+https://github.com/huggingface/nlp
+        pip install git+https://github.com/huggingface/datasets
 
     - name: Are GPUs recognized by our DL frameworks
       run: |

examples/longform-qa/README.md

@@ -1,5 +1,5 @@
 # Long Form Question Answering
 
-This folder contains the code for the Long Form Question answering [demo](http://35.226.96.115:8080/) as well as methods to train and use a fully end-to-end Long Form Question Answering system using the [🤗transformers](https://github.com/huggingface/transformers) and [🤗nlp](https://github.com/huggingface/nlp) libraries.
+This folder contains the code for the Long Form Question answering [demo](http://35.226.96.115:8080/) as well as methods to train and use a fully end-to-end Long Form Question Answering system using the [🤗transformers](https://github.com/huggingface/transformers) and [🤗datasets](https://github.com/huggingface/datasets) libraries.
 
 You can use these methods to train your own system by following along the associate [notebook](https://github.com/huggingface/notebooks/blob/master/longform-qa/Long_Form_Question_Answering_with_ELI5_and_Wikipedia.ipynb) or [blog post](https://yjernite.github.io/lfqa.html).

examples/longform-qa/eli5_app.py

@@ -1,5 +1,5 @@
+import datasets
 import faiss
-import nlp
 import numpy as np
 import streamlit as st
 import torch
@@ -45,7 +45,7 @@ def load_models():
 def load_indexes():
     if LOAD_DENSE_INDEX:
         faiss_res = faiss.StandardGpuResources()
-        wiki40b_passages = nlp.load_dataset(path="wiki_snippets", name="wiki40b_en_100_0")["train"]
+        wiki40b_passages = datasets.load_dataset(path="wiki_snippets", name="wiki40b_en_100_0")["train"]
         wiki40b_passage_reps = np.memmap(
             "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat",
             dtype="float32",
@@ -63,7 +63,7 @@ def load_indexes():
 
 @st.cache(allow_output_mutation=True)
 def load_train_data():
-    eli5 = nlp.load_dataset("eli5", name="LFQA_reddit")
+    eli5 = datasets.load_dataset("eli5", name="LFQA_reddit")
     eli5_train = eli5["train_eli5"]
     eli5_train_q_reps = np.memmap(
         "eli5_questions_reps.dat", dtype="float32", mode="r", shape=(eli5_train.num_rows, 128)

examples/longform-qa/eli5_utils.py

@@ -4,8 +4,8 @@
 from random import choice, randint
 from time import time
 
+import datasets  # noqa: F401
 import faiss  # noqa: F401
-import nlp  # noqa: F401
 import numpy as np
 import pandas as pd
 import torch

examples/requirements.txt

@@ -12,7 +12,7 @@ faiss
 streamlit
 elasticsearch
 pandas
-nlp
+datasets
 fire
 pytest
 conllu

examples/seq2seq/download_wmt.py

@@ -5,25 +5,25 @@
 
 
 def download_wmt_dataset(src_lang="ro", tgt_lang="en", dataset="wmt16", save_dir=None) -> None:
-    """Download a dataset using the nlp package and save it to the format expected by finetune.py
+    """Download a dataset using the datasets package and save it to the format expected by finetune.py
     Format of save_dir: train.source, train.target, val.source, val.target, test.source, test.target.
 
     Args:
         src_lang: <str> source language
         tgt_lang: <str> target language
-        dataset: <str> wmt16, wmt17, etc. wmt16 is a good start as it's small. To get the full list run `import nlp; print([d.id for d in nlp.list_datasets() if "wmt" in d.id])`
+        dataset: <str> wmt16, wmt17, etc. wmt16 is a good start as it's small. To get the full list run `import datasets; print([d.id for d in datasets.list_datasets() if "wmt" in d.id])`
         save_dir: <str>, where to save the datasets, defaults to f'{dataset}-{src_lang}-{tgt_lang}'
 
     Usage:
         >>> download_wmt_dataset('ro', 'en', dataset='wmt16') # saves to wmt16-ro-en
     """
     try:
-        import nlp
+        import datasets
     except (ModuleNotFoundError, ImportError):
-        raise ImportError("run pip install nlp")
+        raise ImportError("run pip install datasets")
     pair = f"{src_lang}-{tgt_lang}"
     print(f"Converting {dataset}-{pair}")
-    ds = nlp.load_dataset(dataset, pair)
+    ds = datasets.load_dataset(dataset, pair)
     if save_dir is None:
         save_dir = f"{dataset}-{pair}"
     save_dir = Path(save_dir)

setup.cfg

@@ -7,6 +7,7 @@ known_first_party = transformers
 known_third_party =
     absl
     conllu
+    datasets
     elasticsearch
     fairseq
     faiss
@@ -16,7 +17,6 @@ known_third_party =
     git
     h5py
     matplotlib
-    nlp
     nltk
     numpy
     packaging

src/transformers/__init__.py

@@ -83,7 +83,7 @@
     add_start_docstrings,
     cached_path,
     is_apex_available,
-    is_nlp_available,
+    is_datasets_available,
     is_psutil_available,
     is_py3nvml_available,
     is_tf_available,

src/transformers/file_utils.py

@@ -66,12 +66,12 @@
 
 
 try:
-    import nlp  # noqa: F401
+    import datasets  # noqa: F401
 
-    _nlp_available = True
+    _datasets_available = True
 
 except ImportError:
-    _nlp_available = False
+    _datasets_available = False
 
 try:
     from torch.hub import _get_torch_home
@@ -155,8 +155,8 @@ def is_torch_tpu_available():
     return _torch_tpu_available
 
 
-def is_nlp_available():
-    return _nlp_available
+def is_datasets_available():
+    return _datasets_available
 
 
 def is_psutil_available():

src/transformers/trainer.py

@@ -20,7 +20,7 @@
 from tqdm.auto import tqdm, trange
 
 from .data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
-from .file_utils import is_nlp_available, is_torch_tpu_available
+from .file_utils import is_datasets_available, is_torch_tpu_available
 from .integrations import (
     default_hp_search_backend,
     is_comet_available,
@@ -65,8 +65,8 @@
     _use_native_amp = True
     from torch.cuda.amp import autocast
 
-if is_nlp_available():
-    import nlp
+if is_datasets_available():
+    import datasets
 
 if is_torch_tpu_available():
     import torch_xla.core.xla_model as xm
@@ -179,10 +179,10 @@ class Trainer:
             :obj:`eval_dataset`. Will default to :func:`~transformers.default_data_collator` if no ``tokenizer`` is
             provided, an instance of :func:`~transformers.DataCollatorWithPadding` otherwise.
         train_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
-            The dataset to use for training. If it is an :obj:`nlp.Dataset`, columns not accepted by the
+            The dataset to use for training. If it is an :obj:`datasets.Dataset`, columns not accepted by the
             ``model.forward()`` method are automatically removed.
         eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
-             The dataset to use for evaluation. If it is an :obj:`nlp.Dataset`, columns not accepted by the
+             The dataset to use for evaluation. If it is an :obj:`datasets.Dataset`, columns not accepted by the
             ``model.forward()`` method are automatically removed.
         tokenizer (:class:`PreTrainedTokenizerBase`, `optional`):
             The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs the
@@ -280,10 +280,10 @@ def __init__(
                 FutureWarning,
             )
 
-        if is_nlp_available():
-            if isinstance(train_dataset, nlp.Dataset):
+        if is_datasets_available():
+            if isinstance(train_dataset, datasets.Dataset):
                 self._remove_unused_columns(self.train_dataset, description="training")
-            if isinstance(eval_dataset, nlp.Dataset):
+            if isinstance(eval_dataset, datasets.Dataset):
                 self._remove_unused_columns(self.eval_dataset, description="evaluation")
 
         self.global_step = None
@@ -294,7 +294,7 @@ def __init__(
         self.hp_search_backend = None
         self.use_tune_checkpoints = False
 
-    def _remove_unused_columns(self, dataset: "nlp.Dataset", description: Optional[str] = None):
+    def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None):
         if not self.args.remove_unused_columns:
             return
         # Inspect model forward signature to keep only the arguments it accepts.
@@ -364,12 +364,12 @@ def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoa
 
         Args:
             eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
-                If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`nlp.Dataset`, columns not
+                If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`, columns not
                 accepted by the ``model.forward()`` method are automatically removed.
         """
         if eval_dataset is None and self.eval_dataset is None:
             raise ValueError("Trainer: evaluation requires an eval_dataset.")
-        elif eval_dataset is not None and is_nlp_available() and isinstance(eval_dataset, nlp.Dataset):
+        elif eval_dataset is not None and is_datasets_available() and isinstance(eval_dataset, datasets.Dataset):
             self._remove_unused_columns(eval_dataset, description="evaluation")
         eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
         eval_sampler = self._get_eval_sampler(eval_dataset)
@@ -393,10 +393,10 @@ def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
 
         Args:
             eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
-                The test dataset to use. If it is an :obj:`nlp.Dataset`, columns not accepted by the
+                The test dataset to use. If it is an :obj:`datasets.Dataset`, columns not accepted by the
                 ``model.forward()`` method are automatically removed.
         """
-        if is_nlp_available() and isinstance(test_dataset, nlp.Dataset):
+        if is_datasets_available() and isinstance(test_dataset, datasets.Dataset):
             self._remove_unused_columns(test_dataset, description="test")
         test_sampler = self._get_eval_sampler(test_dataset)
 
@@ -1200,7 +1200,7 @@ def evaluate(self, eval_dataset: Optional[Dataset] = None) -> Dict[str, float]:
 
         Args:
             eval_dataset (:obj:`Dataset`, `optional`):
-                Pass a dataset if you wish to override :obj:`self.eval_dataset`. If it is an :obj:`nlp.Dataset`,
+                Pass a dataset if you wish to override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`,
                 columns not accepted by the ``model.forward()`` method are automatically removed.
 
         Returns:
@@ -1227,7 +1227,7 @@ def predict(self, test_dataset: Dataset) -> PredictionOutput:
 
         Args:
             test_dataset (:obj:`Dataset`):
-                Dataset to run the predictions on. If it is an :obj:`nlp.Dataset`, columns not accepted by the
+                Dataset to run the predictions on. If it is an :obj:`datasets.Dataset`, columns not accepted by the
                 ``model.forward()`` method are automatically removed.
 
         Returns:

tests/test_trainer.py

@@ -1,6 +1,6 @@
 import unittest
 
-import nlp
+import datasets
 import numpy as np
 
 from transformers import AutoTokenizer, TrainingArguments, is_torch_available
@@ -200,11 +200,11 @@ def test_predict(self):
         x = trainer.eval_dataset.x
         self.assertTrue(np.allclose(preds, 1.5 * x + 2.5))
 
-    def test_trainer_with_nlp(self):
+    def test_trainer_with_datasets(self):
         np.random.seed(42)
         x = np.random.normal(size=(64,)).astype(np.float32)
         y = 2.0 * x + 3.0 + np.random.normal(scale=0.1, size=(64,))
-        train_dataset = nlp.Dataset.from_dict({"input_x": x, "label": y})
+        train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y})
 
         # Base training. Should have the same results as test_reproducible_training
         model = RegressionModel()
@@ -222,7 +222,7 @@ def test_trainer_with_nlp(self):
 
         # Adding one column not used by the model should have no impact
         z = np.random.normal(size=(64,)).astype(np.float32)
-        train_dataset = nlp.Dataset.from_dict({"input_x": x, "label": y, "extra": z})
+        train_dataset = datasets.Dataset.from_dict({"input_x": x, "label": y, "extra": z})
         model = RegressionModel()
         trainer = Trainer(model, args, train_dataset=train_dataset)
         trainer.train()