docs fixing

Helsinki-NLP · Sep 19, 2023 · d783503 · d783503
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diff --git a/docs/source/CONTRIBUTING.md b/docs/source/CONTRIBUTING.md
@@ -5,7 +5,7 @@ OpenNMT-py is a community developed project and we love developer contributions.
 ## Guidelines
 Before sending a PR, please do this checklist first:
 
-- Please run `onmt/tests/pull_request_chk.sh` and fix any errors. When adding new functionality, also add tests to this script. Included checks:
+- Please run `mammoth/tests/pull_request_chk.sh` and fix any errors. When adding new functionality, also add tests to this script. Included checks:
     1. flake8 check for coding style;
     2. unittest;
     3. continuous integration tests listed in `.travis.yml`.

diff --git a/docs/source/FAQ.md b/docs/source/FAQ.md
@@ -236,7 +236,7 @@ Note: all the details about every flag and options for each transform can be fou
 
 Transform name: `filtertoolong`
 
-Class: `onmt.transforms.misc.FilterTooLongTransform`
+Class: `mammoth.transforms.misc.FilterTooLongTransform`
 
 The following options can be added to the configuration :
 - `src_seq_length`: maximum source sequence length;
@@ -246,7 +246,7 @@ The following options can be added to the configuration :
 
 Transform name: `prefix`
 
-Class: `onmt.transforms.misc.PrefixTransform`
+Class: `mammoth.transforms.misc.PrefixTransform`
 
 For each dataset that the `prefix` transform is applied to, you can set the additional `src_prefix` and `tgt_prefix` parameters in its data configuration:
 
@@ -276,7 +276,7 @@ Common options for the tokenization transforms are the following:
 
 Transform name: `onmt_tokenize`
 
-Class: `onmt.transforms.tokenize.ONMTTokenizerTransform`
+Class: `mammoth.transforms.tokenize.ONMTTokenizerTransform`
 
 Additional options are available:
 - `src_subword_type`: type of subword model for source side (from `["none", "sentencepiece", "bpe"]`);
@@ -288,15 +288,15 @@ Additional options are available:
 
 Transform name: `sentencepiece`
 
-Class: `onmt.transforms.tokenize.SentencePieceTransform`
+Class: `mammoth.transforms.tokenize.SentencePieceTransform`
 
 The `src_subword_model` and `tgt_subword_model` should be valid sentencepiece models.
 
 #### BPE ([subword-nmt](https://github.com/rsennrich/subword-nmt))
 
 Transform name: `bpe`
 
-Class: `onmt.transforms.tokenize.BPETransform`
+Class: `mammoth.transforms.tokenize.BPETransform`
 
 The `src_subword_model` and `tgt_subword_model` should be valid BPE models.
 
@@ -321,7 +321,7 @@ These different types of noise can be controlled with the following options:
 
 Transform name: `switchout`
 
-Class: `onmt.transforms.sampling.SwitchOutTransform`
+Class: `mammoth.transforms.sampling.SwitchOutTransform`
 
 Options:
 
@@ -331,7 +331,7 @@ Options:
 
 Transform name: `tokendrop`
 
-Class: `onmt.transforms.sampling.TokenDropTransform`
+Class: `mammoth.transforms.sampling.TokenDropTransform`
 
 Options:
 
@@ -341,7 +341,7 @@ Options:
 
 Transform name: `tokenmask`
 
-Class: `onmt.transforms.sampling.TokenMaskTransform`
+Class: `mammoth.transforms.sampling.TokenMaskTransform`
 
 Options:
 
@@ -427,7 +427,7 @@ The `example` argument of `apply` is a `dict` of the form:
 }
 ```
 
-This is defined in `onmt.inputters.corpus.ParallelCorpus.load`. This class is not easily extendable for now but it can be considered for future developments. For instance, we could create some `CustomParallelCorpus` class that would handle other kind of inputs.
+This is defined in `mammoth.inputters.corpus.ParallelCorpus.load`. This class is not easily extendable for now but it can be considered for future developments. For instance, we could create some `CustomParallelCorpus` class that would handle other kind of inputs.
 
 
 ## Can I get word alignments while translating?
@@ -649,7 +649,7 @@ A server configuration file (`./available_models/conf.json`) is required. It con
 ### II. How to start the server without Docker ?
 ---
 ##### 0. Get the code
-The translation server has been merged into onmt-py `master` branch.   
+The translation server has been merged into mammoth-py `master` branch.   
 Keep in line with master for last fix / improvements.
 ##### 1. Install `flask`
 ```bash
@@ -699,7 +699,7 @@ RUN pip install --no-cache-dir -r requirements.txt
 COPY server.py ./
 COPY tools ./tools
 COPY available_models ./available_models
-COPY onmt ./onmt
+COPY mammoth ./mammoth
 
 CMD ["python", "./server.py"]
 ```

diff --git a/docs/source/examples/LanguageModelGeneration.md b/docs/source/examples/LanguageModelGeneration.md
@@ -106,7 +106,7 @@ Options contained in the loaded model will trigger language modeling specific in
 head data/wikitext-103-raw/wiki.valid.bpe | cut -d" " -f-15 > data/wikitext-103-raw/lm_input.txt
 ```
 
-To proceed with LM inference, sampling methods such as top-k sampling or nucleus sampling are usually applied. Details and options about inference methods can be found in [`onmt/opts.py`](https://github.com/OpenNMT/OpenNMT-py/tree/master/onmt/opts.py).
+To proceed with LM inference, sampling methods such as top-k sampling or nucleus sampling are usually applied. Details and options about inference methods can be found in [`mammoth/opts.py`](https://github.com/OpenNMT/OpenNMT-py/tree/master/mammoth/opts.py).
 
 The following command will provide inference with nucleus sampling of p=0.9 and return the 3 sequences with the lowest perplexity out of the 10 generated sequences:
 ```bash

diff --git a/docs/source/examples/Library.ipynb b/docs/source/examples/Library.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The example notebook (available [here](https://github.com/OpenNMT/OpenNMT-py/blob/master/docs/source/examples/Library.ipynb)) should be able to run as a standalone execution, provided `onmt` is in the path (installed via `pip` for instance).\n",
+    "The example notebook (available [here](https://github.com/OpenNMT/OpenNMT-py/blob/master/docs/source/examples/Library.ipynb)) should be able to run as a standalone execution, provided `mammoth` is in the path (installed via `pip` for instance).\n",
     "\n",
     "Some parts may not be 100% 'library-friendly' but it's mostly workable."
    ]
@@ -42,12 +42,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import onmt\n",
-    "from onmt.inputters.inputter import _load_vocab, _build_fields_vocab, get_fields, IterOnDevice\n",
-    "from onmt.inputters.corpus import ParallelCorpus\n",
-    "from onmt.inputters.dynamic_iterator import DynamicDatasetIter\n",
-    "from onmt.translate import GNMTGlobalScorer, Translator, TranslationBuilder\n",
-    "from onmt.utils.misc import set_random_seed"
+    "import mammoth\n",
+    "from mammoth.inputters.inputter import _load_vocab, _build_fields_vocab, get_fields, IterOnDevice\n",
+    "from mammoth.inputters.corpus import ParallelCorpus\n",
+    "from mammoth.inputters.dynamic_iterator import DynamicDatasetIter\n",
+    "from mammoth.translate import GNMTGlobalScorer, Translator, TranslationBuilder\n",
+    "from mammoth.utils.misc import set_random_seed"
    ]
   },
   {
@@ -75,7 +75,7 @@
    ],
    "source": [
     "# enable logging\n",
-    "from onmt.utils.logging import init_logger, logger\n",
+    "from mammoth.utils.logging import init_logger, logger\n",
     "init_logger()"
    ]
   },
@@ -214,7 +214,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from onmt.utils.parse import ArgumentParser\n",
+    "from mammoth.utils.parse import ArgumentParser\n",
     "parser = ArgumentParser(description='build_vocab.py')"
    ]
   },
@@ -224,7 +224,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from onmt.opts import dynamic_prepare_opts\n",
+    "from mammoth.opts import dynamic_prepare_opts\n",
     "dynamic_prepare_opts(parser, build_vocab_only=True)"
    ]
   },
@@ -279,7 +279,7 @@
     }
    ],
    "source": [
-    "from onmt.bin.build_vocab import build_vocab_main\n",
+    "from mammoth.bin.build_vocab import build_vocab_main\n",
     "build_vocab_main(opts)"
    ]
   },
@@ -382,8 +382,8 @@
     {
      "data": {
       "text/plain": [
-       "{'src': <onmt.inputters.text_dataset.TextMultiField at 0x7fca93802c50>,\n",
-       " 'tgt': <onmt.inputters.text_dataset.TextMultiField at 0x7fca93802f60>,\n",
+       "{'src': <mammoth.inputters.text_dataset.TextMultiField at 0x7fca93802c50>,\n",
+       " 'tgt': <mammoth.inputters.text_dataset.TextMultiField at 0x7fca93802f60>,\n",
        " 'indices': <torchtext.data.field.Field at 0x7fca93802940>}"
       ]
      },
@@ -478,29 +478,29 @@
     "rnn_size = 500\n",
     "# Specify the core model.\n",
     "\n",
-    "encoder_embeddings = onmt.modules.Embeddings(emb_size, len(src_vocab),\n",
+    "encoder_embeddings = mammoth.modules.Embeddings(emb_size, len(src_vocab),\n",
     "                                             word_padding_idx=src_padding)\n",
     "\n",
-    "encoder = onmt.encoders.RNNEncoder(hidden_size=rnn_size, num_layers=1,\n",
+    "encoder = mammoth.encoders.RNNEncoder(hidden_size=rnn_size, num_layers=1,\n",
     "                                   rnn_type=\"LSTM\", bidirectional=True,\n",
     "                                   embeddings=encoder_embeddings)\n",
     "\n",
-    "decoder_embeddings = onmt.modules.Embeddings(emb_size, len(tgt_vocab),\n",
+    "decoder_embeddings = mammoth.modules.Embeddings(emb_size, len(tgt_vocab),\n",
     "                                             word_padding_idx=tgt_padding)\n",
-    "decoder = onmt.decoders.decoder.InputFeedRNNDecoder(\n",
+    "decoder = mammoth.decoders.decoder.InputFeedRNNDecoder(\n",
     "    hidden_size=rnn_size, num_layers=1, bidirectional_encoder=True, \n",
     "    rnn_type=\"LSTM\", embeddings=decoder_embeddings)\n",
     "\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "model = onmt.models.model.NMTModel(encoder, decoder)\n",
+    "model = mammoth.models.model.NMTModel(encoder, decoder)\n",
     "model.to(device)\n",
     "\n",
     "# Specify the tgt word generator and loss computation module\n",
     "model.generator = nn.Sequential(\n",
     "    nn.Linear(rnn_size, len(tgt_vocab)),\n",
     "    nn.LogSoftmax(dim=-1)).to(device)\n",
     "\n",
-    "loss = onmt.utils.loss.NMTLossCompute(\n",
+    "loss = mammoth.utils.loss.NMTLossCompute(\n",
     "    criterion=nn.NLLLoss(ignore_index=tgt_padding, reduction=\"sum\"),\n",
     "    generator=model.generator)"
    ]
@@ -520,7 +520,7 @@
    "source": [
     "lr = 1\n",
     "torch_optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n",
-    "optim = onmt.utils.optimizers.Optimizer(\n",
+    "optim = mammoth.utils.optimizers.Optimizer(\n",
     "    torch_optimizer, learning_rate=lr, max_grad_norm=2)"
    ]
   },
@@ -681,7 +681,7 @@
     {
      "data": {
       "text/plain": [
-       "<onmt.utils.statistics.Statistics at 0x7fca934e8e80>"
+       "<mammoth.utils.statistics.Statistics at 0x7fca934e8e80>"
       ]
      },
      "execution_count": 28,
@@ -690,10 +690,10 @@
     }
    ],
    "source": [
-    "report_manager = onmt.utils.ReportMgr(\n",
+    "report_manager = mammoth.utils.ReportMgr(\n",
     "    report_every=50, start_time=None, tensorboard_writer=None)\n",
     "\n",
-    "trainer = onmt.Trainer(model=model,\n",
+    "trainer = mammoth.Trainer(model=model,\n",
     "                       train_loss=loss,\n",
     "                       valid_loss=loss,\n",
     "                       optim=optim,\n",
@@ -726,9 +726,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "src_data = {\"reader\": onmt.inputters.str2reader[\"text\"](), \"data\": src_val}\n",
-    "tgt_data = {\"reader\": onmt.inputters.str2reader[\"text\"](), \"data\": tgt_val}\n",
-    "_readers, _data = onmt.inputters.Dataset.config(\n",
+    "src_data = {\"reader\": mammoth.inputters.str2reader[\"text\"](), \"data\": src_val}\n",
+    "tgt_data = {\"reader\": mammoth.inputters.str2reader[\"text\"](), \"data\": tgt_val}\n",
+    "_readers, _data = mammoth.inputters.Dataset.config(\n",
     "    [('src', src_data), ('tgt', tgt_data)])"
    ]
   },
@@ -738,9 +738,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dataset = onmt.inputters.Dataset(\n",
+    "dataset = mammoth.inputters.Dataset(\n",
     "    vocab_fields, readers=_readers, data=_data,\n",
-    "    sort_key=onmt.inputters.str2sortkey[\"text\"])"
+    "    sort_key=mammoth.inputters.str2sortkey[\"text\"])"
    ]
   },
   {
@@ -749,7 +749,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_iter = onmt.inputters.OrderedIterator(\n",
+    "data_iter = mammoth.inputters.OrderedIterator(\n",
     "            dataset=dataset,\n",
     "            device=\"cuda\",\n",
     "            batch_size=10,\n",
@@ -766,8 +766,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "src_reader = onmt.inputters.str2reader[\"text\"]\n",
-    "tgt_reader = onmt.inputters.str2reader[\"text\"]\n",
+    "src_reader = mammoth.inputters.str2reader[\"text\"]\n",
+    "tgt_reader = mammoth.inputters.str2reader[\"text\"]\n",
     "scorer = GNMTGlobalScorer(alpha=0.7, \n",
     "                          beta=0., \n",
     "                          length_penalty=\"avg\", \n",
@@ -779,7 +779,7 @@
     "                        tgt_reader=tgt_reader, \n",
     "                        global_scorer=scorer,\n",
     "                        gpu=gpu)\n",
-    "builder = onmt.translate.TranslationBuilder(data=dataset, \n",
+    "builder = mammoth.translate.TranslationBuilder(data=dataset, \n",
     "                                            fields=vocab_fields)"
    ]
   },